Quality of the
Decennial Census
for Asian Americanand Native Hawaiian
and Pacific Islander
Communities
An Expanded
ApproachAbout AAJC
Advancing Justice | AAJC is a national 501(c)(3) nonprofit founded in 1991 in Washington, D.C.
Advancing Justice | AAJC’s mission is to advance the civil and human rights of Asian
Americans and to build and promote a fair and equitable society for all. Through our census
work, Advancing Justice | AAJC has served as a member of numerous advisory committees to
the Census Bureau since 2000, including most recently the National Advisory Committee on
Racial, Ethnic, and Other Populations.
In addition, Advancing Justice | AAJC currently co-chairs the Leadership Conference on Civil
and Human Rights’ Census Task Force. Advancing Justice | AAJC has conducted national,
state, and local outreach and educational projects focused on the Asian American, Native
Hawaiian, and Pacific Islander communities for Census 2000, Census 2010, and Census 2020and has also litigated issues surrounding Census 2020.

About Demographic Analytics Advisors
Demographic Analytics Advisors is a demographic, data science, and analytics consulting firm
serving non-profit and public sector clients throughout the United States. We believe in the
deep importance of public data sources, especially the decennial census, and spend much of
our time thinking about and working on issues of census quality and usability. When we are
not thinking about or working on these issues, we are putting public data sources to work forour state and local government clients to help them better serve their constituents. For
further information, please see our website: https://www.demoadvisors.com/

CONTENTS
EXECUTIVE SUMMARYii

INTRODUCTION

1MEASURE OF CENSUS QUALITY

10

Demographic Analysis (DA)10

Post-Enumeration Survey (PES)15

A New Approach: Population Estimates Analysis (PA)24

ANALYSIS: THE QUALITY OF CENSUS COUNTS FORASIAN AMERICAN AND NHPI COMMUNITIES35

Asian American and NHPI Communities: 2000 Census

35

Asian American Community: 2010 and 2020 Censuses

38

Local Snapshots: Asian American Case Studies

44

NHPI Community: 2010 and 2020 Censuses

58

Local Snapshots: NHPI Case Studies

66

RECOMMENDATIONS

87

CONCLUSIONS

92

APPENDIX

94i

Executive Summary
The census is the foundation of our democracy. The U.S.
Constitution mandates census data collection to apportion seats in the
U.S. House of Representatives and to redraw district lines at all levels ofgovernment. The government also uses census data to distribute federal
and state funding. Despite the central importance an accurate decennial
census plays in our democracy, the census lacks data on census coveragefor Asian American and Native Hawaiian and Pacific Islander (NHPI)
communities at geographies below the national level.

While Asian American and NHPI communities were
overcounted nationally, some states had undercounts in both2010 and 2020. This is a problem because, despite a reported
national overcount of these communities in 2020, some Asian
American and NHPI communities were still undercounted atlower levels of geography. Moreover, these overcounts andundercounts were not evenly distributed geographically, as
shown in Figure i, or in terms of age, as seen in Figures ii and iii.

While both Asian American and NHPI communities were overcounted in
the 2010 and 2020 Censuses, most counties were actually undercounted.
This tells us that the counties with reported estimated overcounts had
substantially larger overcounts of Asian American or NHPI communities
than counties that were undercounted. For small communities, including
the NHPI community, no single method can currently provide reliablemeasures of coverage. Further work is required to create more stableestimates of coverage for this group.

ii

Figure i2000 Census Accuracy: AANHPI Alone

43%

55%

2%

of counties
overcountedof counties
undercountedof counties
no difference

Asian Americans and NHPIs were overcounted
or had net coverage that was not statisticallydifferent from zero in the 2000, 2010, and 2020
Censuses at the national level. However, ineach decade more counties were actually
undercounted than overcounted.

2010 Census Accuracy: Asian Alone2010 Census Accuracy: NHPI Alone

30%

68%

2%

42%

49%

9%

of counties
overcountedof counties
undercountedof counties
no differenceof counties
overcountedof counties
undercountedof counties
no difference2020 Census Accuracy: Asian Alone

2020 Census Accuracy: NHPI Alone

30%

68%

2%

36%

55%

9%

of counties
overcountedof counties
undercountedof counties
no differenceof counties
overcountedof counties
undercountedof counties
no difference

Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimates data for 2000, 2010, and 2020.

Figure ii

Coverage by Age Group, 2010
Asian American, Alone and Alone or in Combination Populations

Error of Closure (%)

Alone

Alone or
Alone in Combination?
To capture the growing

Alone or in Combination20

biracial and multiracial
population, people can10check more than one of
the available races whenfilling out the Census.
overcount0

undercount

-10

20 9
-2
4
25
-2
9
30
-3
4
35
-3
9
40
-4
4
45
-4
9
50
-5
4
55
-5
9
60
-6
4
65
-6
9
70
-7
4
75
-7
9
80
-8
4
85
+

-1

4
-1

15

9
5-

10

4
0-

-20

Age
Source: Authors' calculations using United States Census Bureau decennial census and postcensal population
estimates data for 2000, 2010, and 2020.

Asian American coverage differs significantly by age. Undercounts exist for the
youngest (ages 0–4) and oldest (ages 75 and over), while young adults and working
age adults are persistently overcounted.

iii

The Census Bureau
describes this as either“Race Alone” or “RaceAlone or in Combination.”
Whenever we use Race
Alone, we mean those who
only marked the specificrace category. When we
say Race Alone or in
Combination, we mean
anyone who marked thatspecific race category,
regardless of what otherraces they may have also
selected.

Figure iii

Coverage by Age Group, 2010
NHPI, Alone and Alone or in Combination Populations
Alone

Alone or in Combination

Error of Closure (%)

20

overcount0

undercount

-20

25 4
-2
9
30
-3
4
35
-3
9
40
-4
4
45
-4
9
50
-5
4
55
-5
60 9
-6
4
65
-6
9
70
-7
4
75
-7
9
80
-8
4
85
+

20

-2

9

4

-1
15

9

-1

10

5-

0-

4

-40

Age
Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimates data for
2000, 2010, and 2020.

NHPI coverage differs significantly by age. The oldest (ages 65 and above) are undercounted,
while all other ages are persistently overcounted. This is significant because, unlike other racegroups, there is not an undercount of young NHPI children.

Key Findings
1. Census Coverage for Asian Americans and NHPIs: Despite a national
overcount of Asian Americans and NHPIs, some Asian Americans and
NHPIs were still undercounted.
The national overcount of Asian Americans and NHPIs masked
important variation in Asian American and NHPI coverage. Significant
overcounts in some places concealed undercounts in others. When
Asian American and NHPI communities are undercounted, they not only
lose political representation, they also miss out on valuable resourcesthat would have otherwise been directed towards their communities.
2. Response Rates and Geography: Not all Asian American and NHPI
communities respond to the census at the same rate; instead, response
rates vary geographically.
While Asian American and NHPI communities were overcounted
nationally, some states had undercounts in both 2010 and 2020. Somestates in the Mountain West and in the South showed potentialiv

undercounts for Asian Americans, while states on the coasts, in theUpper Midwest, and in the Southwest generally showed estimatedovercounts for the same group. Similarly, many states with high NHPI
populations followed the national pattern with estimated overcounts inboth 2010 and 2020. However, there were potential NHPI undercounts insome states in the Upper Midwest, the Northeast, and the South. If we
dig down to the county level, we even saw some counties with large
NHPI communities, such as Los Angeles and Hawaii counties, showing
estimated undercounts in 2020. These unexpected differences could be
due to many factors, including the fact that populations in these areas
tend to be more mobile, more likely to be renters, and in some cases
have a higher proportion of non-citizens.
3. Coverage Errors and Age: Net coverage errors do vary by age for
Asian Americans and NHPIs.
The undercount of young children common in other racial and ethnic
groups also exists among Asian Americans, though it is not evenly
distributed throughout the country. In contrast, NHPI young children are
not undercounted. Additional research should be conducted todetermine why.

How We Measure the Accuracy of Asian American and NHPI
Census Counts
We uncover Asian American and NHPI undercounts by utilizing amethodology that compares postcensal population estimates––theofficial population number for the United States in every year that does
not have a decennial census––to decennial census data. We call thisapproach Population Estimates Analysis (PA) (See Figure iv). This allows
us to identify the geographic distribution of potential overcounts andundercounts for Asian American and NHPI communities, illustrated by
Figure iv. In so doing, we build upon traditional measures of census
quality such as Demographic Analysis (DA) and Post-Enumeration
Surveys (PES). DA, long touted as the only truly independent measure ofdecennial census quality, fails to provide any data on Asian Americanand NHPI communities. The PES, which retroactively surveys the
population to determine who correctly responded (or did not respond)v

Figure iv

Population Estimates Analysis (PA)the what and why

What? The error of closure is another way to
say the difference between population
estimates and the decennial census as ofcensus day (April 1 of 2000, 2010, and 2020).

Why? Knowing the direction of these differences
can potentially help us to understand the quality ofthe decennial census. However, the differences canactually be due to three different errors:
The first error is the one we are interested inunderstanding: the error in the current decennial
census.

Error of
ClosureDecennial
Census

The second error can be the base that thepostcensal estimates are built off of (i.e., the lastdecennial census).

Population
Estimates

The third error could be found in the data or
methods that are used to estimate change
between the prior and the current census.

Historically, most analysis of the error of closure in the United States aims tounderstand errors in the base (2) or errors in the data or methods (3). As thepostcensal population estimates have improved over the decades, they have becomea better indicator of potential undercounts and overcounts––therefore helping us tobetter understand the error in the current decennial census (1).

to the decennial census, only provides coverage for the Asian Americanand NHPI communities at the national level.
PA helps address the challenges of applying traditional census quality
measures to Asian American and NHPI communities. Figure v compares
the strengths, limitations, and impact of traditional measures of census
quality and our approach, PA. Though data precision prevents us frommaking claims about exact overcounts and undercounts for Asian
Americans or NHPIs in the United States at the county level, we canshow differential patterns throughout the country. Therefore, westrongly caution against making decisions based on national level
numbers and recommend changes to Census Bureau research andplanning, implementation, and review of census coverage measurement
to improve census accuracy.

vi

Figure v

Understanding Census Quality
Figure v

Demographic Analysis (DA)Near-complete independence from decennial census
Uses high quality administrative data sources

Strengths
Limitations

Lack of coverage below the national level
Lack of coverage of most race groups
Can only look at net coverage
No additional information about Asian American and NHPI communities

ImpactPost-Enumeration Survey (PES)Strengths
Relatively independent from the decennial census
More granular look at census coverage by state and demographic characteristics
Helps identify gross coverage
Potential for correlated errors and bias
Only as good as the frame and execution of the sample survey
Lacks estimates of coverage by race and ethnicity at the subnational level
Some additional information about Asian American and NHPI communities
Shows relatively good net undercount and overcount numbers for Asian
American community, along with NHPI community (though the margins oferror for NHPI are quite high)Though net numbers are near zero, still significant duplications and omissions
that require further understanding and study

Limitations

ImpactPopulation Estimates Analysis (PA): Our Approach
Identify potential undercounts and overcounts at the county level by race,and for all race groups by age
Strong for identifying patterns that will signal areas for future research
Higher error than DA
Unknown if error comes from the current census, prior census, orcomponents of change

Strengths

Limitations

ImpactCoverage for the Asian American and NHPI communities varies throughout thecountry
Differences for the NHPI community are large, showing the difficulties of coverage
analysis for smaller racial groups, requiring more investment
Coverage for the Asian American and NHPI communities varies by age, and likely
subgroup (though this is harder to support with available data– see case studies)vii

Why Assessing the Accuracy of the Census Count Matters
Asian American and NHPI communities’ decennial census data quality
matters. Beyond ensuring accurate political representation for the next
decade, understanding the differential coverage of these groups allows
for better planning and outreach for the next decennial census.
It also facilitates a better awareness of the internal variation in coverage
within Asian American and NHPI communities. Asian American and
NHPI communities have been among the fastest growing over the last
several decades. Often viewed as a monolith, Asian Americans and
NHPIs are highly diverse, including several dozen detailed subgroups
that can differ dramatically across key social and economic indicators.
The Asian American population is a majority immigrant community,
while one in six Pacific Islanders are foreign-born and, depending on
their country of birth, may hold different types of immigration statuses.
Native Hawaiians and many Pacific Islanders born in Hawaii, Guam, or
the Commonwealth of the Northern Mariana Islands are U.S. citizens.
Through a better understanding about census coverage of and within
Asian American and NHPI communities, the decennial census can offer
insight into the specific needs and concerns of different racial groups
while simultaneously enhancing planning and outreach for future
censuses.
Detailed information on regional and subgroup variation in coveragefurther ensures that the Census Bureau, advocacy partners, and local
government partners can accurately budget for and spend funds onachieving fair and accurate counts for both communities. Finally,
identifying this coverage allows us to better assess potential deficiencies
in previous census’ representation for Asian Americans and NHPIs,
which in turn will allow us to track trends in coverage and identify
potential causes of inaccuracies.
The reasons why some communities have overcounts and others haveundercounts is beyond the scope of this report. However, areas withlarger potential undercounts tend to have smaller Asian American or
NHPI communities––and they tend to be newer, emerging
communities in these states. Further investigation can help uncover
why different communities experience undercounts or overcounts.

viii

Key Takeaway
The Census Bureau continues to miss Asian Americans and NHPIs in thedecennial census. While these missing numbers have been offset by
duplicate responses at the national level, assessing census accuracy byrace at lower geographic levels illustrates that those missed and those
overcounted are often coming from different neighborhoods. As aresult, those most likely to be missed continue to lose out on their fairshare of resources and political representation. As planning for the 2030
Census moves forward, it is imperative that policymakers, the Census
Bureau, and other interested parties recognize that Asian Americans
and NHPIs continue to risk being missed in the census and othersurveys. Thus, they must plan for a more accurate count by making
policy decisions and outreach plans that mitigate the risk of overcounts
and undercounts of Asian Americans and NHPIs. The following
recommendations provide a roadmap toward that end goal.

Recommendations
The Census Bureau and other stakeholders can take steps to mitigate
the risk of overcounts and undercounts. These broadly applicable
recommendations apply to and extend beyond Asian Americans and
NHPIs. Our chronologically organized recommendations offer
suggestions for improvements before, during, and after the fielding of acensus.
1. Census Coverage for Asian Americans and NHPIs: Despite a national
overcount of Asian Americans and NHPIs, some Asian Americans and
NHPIs were still undercounted.
Research how Asian American and NHPI communities can beincorporated into DA. Can we use available data sources to build a
DA for the Asian American and NHPI communities?
Develop a more robust plan for the PES in 2030 by incorporating
input from stakeholder communities well in advance of launching
the PES. This plan should include an increased budget to provide
subnational estimates of coverage for race groups, at least above areasonable population threshold.

ix

Focus on a larger set of potential census quality measures, andmaintain transparency in how the Census Bureau measures quality.
This should include, at a minimum, a discussion of how our approach,PA, can be incorporated into analyses of census coverage.
2. Implementing the Census: Applying Research Findings to the 2030
CensusContinue and enhance the promotion of Get Out the Count (GOTC)
strategies for different subgroups. No group is a monolith, and a onesize-fits-all approach to census outreach will result in further
inaccuracies and wasted resources.
Continue to enhance operations and the targeted use ofadministrative data. While administrative data should be used in
responsible and cost-effective ways, the Census Bureau should take apeople-first approach and attempt to secure a response directly fromthe household. Generally, administrative records should be a lastresort. Administrative data should lower the cost of the census for
those that are easily counted in administrative records (and most
likely in the census), while ensuring that the funding “saved” goes toefforts to enumerate those who are hardest to count by increasing
outreach and Nonresponse Followup.
3. After Fielding the Census: Executing Best Practices in Post-Fielding
Activities to Improve the Mechanics of the Count
Continue to develop best practices for processing and resolving
omissions and duplications. Along with its ongoing focus onaddressing omissions, the Census Bureau should research how tobetter address and correct duplications or other erroneous
enumerations that lead to overcounts.
The Census Bureau should research and implement corrections fordifferential undercounts by race at the lowest level of geography
possible. The Census Bureau has shown willingness to take on thistough problem through improvements to the population estimatesbase by using DA results by age. But much more should be done.

xThe Census Bureau should formally and publicly debrief and discussplans for how to address these issues with all relevant stakeholders.
This would include, but not be limited to, the Census National
Advisory Committee and Census Scientific Advisory Committee.
The Census Bureau should increase investment in partnering withacademics and other researchers to determine the causes ofovercounts and undercounts, particularly as they impact different
racial and ethnic groups. More investment is needed to understandthe specific barriers to participation for differential racial and ethnic
groups, as well as the subgroups within.

For more information, and our citations, please see our full report:
https://www.advancingjustice-aajc.org/publication/quality-decennial-census-asianamerican-and-native-hawaiian-and-pacific-islanderxiIntroduction
Knowing the quality of the decennial census for Asian American and NHPI
communities is of paramount importance. The decennial census is our once-in-adecade chance to accurately count the entire population living in the UnitedStates, regardless of their citizenship status, housing, or lack thereof. The census
ensures correct political representation for the next decade¹ and the disbursementof $1.5 trillion of federal funding annually.² Learning about the differential coverageof Asian Americans and NHPIs––when compared to other racial and ethnic
groups––allows for better planning and outreach for the next decennial census.³
It also improves our understanding of the internal variation in coverage within
Asian American and NHPI communities. Detailed information on regional variationin coverage further ensures that the Census Bureau and their advocacy and local
government partners can properly budget for and spend funds on achieving thebest overall count for Asian Americans and NHPIs. Finally, analyzing census
coverage allows us to better assess potential deficiencies in previous censuses'
representation for Asian Americans and NHPIs, which in turn will allow us to track
trends in coverage and identify potential causes of inaccuracies.

Explaining Components of Coverage and Implications
Coverage: The goal of the decennial census is “to counteveryone once, only once, and in the right place.”⁴
Census coverage is the effectiveness or accuracy of acensus.
Net Coverage (Undercount or Overcount): Duplicatesand omissions are two sides of the coverage equation. If
duplicates outnumber omissions, a net overcount exists.
If omissions outnumber duplicates, a net undercountoccurs. The net undercount or overcount doesn’t reveal
anything about the number of duplications or omissions.
Rather, it indicates how out of balance these figures are.
A net undercount of zero does not necessarily mean
there was an accurate count. Instead, it could mean that
there were an equal number of people counted multiple
times as there were people missed.

1This report discusses net
coverage. Certain
methods of measuring
the accuracy of thedecennial census allow
us to understand themore detailed
components of this net
coverage measure.

Introduction

Asian American and NHPI communities have been among the fastest growing
over the last several decades.⁵ Asian Americans grew 38.6% and NHPIs by 29.5%
between 2010 and 2020, and by 46% and 40% respectively between 2000 and2010.⁶ Asian Americans and NHPIs are often viewed as monolithic groups. Toooften, no distinction is made between Asian Americans and NHPIs. Others
recognize the difference between these groups but fail to distinguish between
different Asian American or NHPI subgroups. In reality, Asian Americans and
NHPIs are highly diverse, including more than 25 subgroups for Asian Americans
and more than 25 subgroups for NHPIs⁷ that can differ dramatically across key
social and economic indicators.⁸

Components of Coverage

Erroneous Enumerations (Duplicates):
Sometimes people are counted more thanCorrect Enumerations: People who theonce in a census. The Census Bureau has adecennial census counted once, only once,

process for discerning which records arein the right place. Correct enumerations––

duplicates and which of the two duplicatesas well as other components of coverage––should be counted, but it is not perfect. The

can only be estimated from the Post-

Census Bureau has very high standards for

Enumeration Survey (PES).

dropping a duplicate response, and many of
their procedures for doing so require costly

Erroneous Enumerations (Other):follow up with households.⁹ For example, a

In addition to duplicates, people who werecollege student might be counted both atborn after Census Day or died before

their college dorm and at home with their

Census Day (April 1 of the year of theparents.

decennial census) are also considerederroneous enumerations. Foreign tourists

Omissions: Despite the Census Bureau’s

are sometimes enumerated accidentally

best efforts, some people are still not

and are considered other erroneous

counted in the decennial census. Omissions

enumerations. While not exhaustive, theseinclude people who are not captured in self-

examples provide some of the reasons why

response, Nonresponse Followup, ora person should not have been counted.

imputation.

Whole Person Imputations: If the Census Bureau does not receive a response with enoughcharacteristics of a person or the number of people in a household, they will use a statisticaltechnique to make an educated guess about the number of people in a household and theircharacteristics. These imputations mostly occur when a household member or a proxy
respondent (i.e., someone outside the household such as a neighbor) provided a household
count without additional information on the household members.
2Introduction

Ensuring that analyses are provided separately for Asian American and NHPI
communities whenever possible is critical to understanding the specific needs
and concerns of different racial groups. For example, the Asian American
population is a majority immigrant community, with approximately two-thirds ofAsian Americans born outside of the United States. According to our 2019 report,“The Asian American immigrant population grew four times as fast as the total
population between 2010 and 2017.”¹⁰ Moreover, historian Erika Lee notes, “Asian
American history begins long before the United States was even a country.”¹¹
Despite this fact, Asian Americans were largely barred from immigrating to the
U.S. for many years, starting with the Chinese Exclusion Act of 1882 and the
National Origins Act of 1924. This lasted until the Immigration and Nationality Act
of 1965 abolished the immigration system based on national origin quotas, which
heavily favored immigration from Northern and Western Europe. Since then,people from Asia and other parts of the world could immigrate to the UnitedStates. The family-based immigration system, combined with waves of Asian
refugees who would later sponsor relatives, has resulted in the tremendous
growth of Asian American communities.
While Native Hawaiians and many Pacific Islanders born in Hawai‘i, Guam, or the
Commonwealth of the Northern Mariana Islands are U.S. citizens, one in six PacificIslanders are foreign-born. Depending on their country of birth, they may holddifferent types of immigration statuses. For example, some Pacific Islanders are
considered U.S. nationals because they come from U.S. territories. Others are
considered Compact of Free Association migrants.¹² Still others are considered
foreign nationals from countries without an association with the United States
who must apply for legal permanent resident (LPR) status to move to the UnitedStates.¹³
These nuances matter. For example, both Asian American and NHPI communities
would have concerns with attempts to add a citizenship question to thedecennial census. But different subgroups would have different concerns. Thus,
messaging and outreach would need to be tailored to address specific concerns
and to encourage full participation in the decennial census.

Key Findings
1. Census Coverage for Asian Americans and NHPIs: Despite a national overcountof Asian Americans and NHPIs, some Asian Americans and NHPIs were still
undercounted.

3

Introduction

The national overcount of Asian Americans and NHPIs masked importantvariation in Asian American and NHPI coverage. Significant overcounts in someplaces concealed undercounts in others. When Asian American and NHPI
communities are undercounted, they not only lose political representation, theyalso miss out on valuable resources that would have otherwise been directed
towards their communities.
2. Response Rates and Geography: Not all Asian American and NHPI
communities respond to the census at the same rate. Instead, response rates
vary geographically.
While Asian American and NHPI communities were overcounted nationally,some states had undercounts in both 2010 and 2020. Some states in the
Mountain West and in the South showed potential undercounts for Asian
Americans, while states on the coasts, in the Upper Midwest, and in theSouthwest generally showed estimated overcounts for the same group. Similarly,
many states with high NHPI populations followed the national pattern withestimated overcounts in both 2010 and 2020. However, there were potential NHPI
undercounts in some states in the Upper Midwest, the Northeast, and the South.
If we dig down to the county level, we even saw some counties with large NHPI
communities, such as Los Angeles and Hawaii counties, showing estimatedundercounts in 2020.¹⁴
Geography (and by extension characteristics such as race, income, language, andeducation) can serve as a proxy for more nebulous concepts like social cohesion
and community ties. This may have an impact on census quality because when aperson's neighbors, friends, and community members respond to the census, it ismore likely that they too will respond. Therefore, we can examine regional
differentials in census response rates to better understand how we might
improve the count for future censuses.
3. Coverage Errors and Age: Net coverage errors do vary by age for Asian
Americans and NHPIs.
The undercount of young children common in other racial and ethnic groups also
exists among Asian Americans, though it is not evenly distributed throughout thecountry. In contrast, NHPI young children are not undercounted. Additional
research should be conducted to determine why.
Age is a very predictive indicator of response to a decennial census, similar to raceand ethnicity. Assessing undercounts matters because government4

Introduction

organizations, nonprofits, and private industry all use data by age. Examples
include: public health indicators, school enrollment forecasts, and numerous
program participation rates for government and non-government programs.

How We Measure the Accuracy of Asian American and NHPI Census
CountsTraditional measures of decennial census quality, namely DA and the PES, only
give advocates, researchers, and policymakers a glimpse into census coverage of
Asian American and NHPI communities.¹⁵ DA, long touted as the only truly
independent measure of decennial census quality, is silent on coverage for Asian
American and NHPI communities.¹⁶ The PES, which retroactively surveys the
population to determine who correctly responded (or did not respond) to thedecennial census, only provides national coverage for the Asian American and
NHPI communities.¹⁷ Therefore, we lack subnational data on census coverage for
Asian American and NHPI communities.
PA compares the postcensal population estimates with the decennial census.¹⁸
The postcensal population estimates are the official population numbers for theUnited States in every year that does not have a decennial census.¹⁹ The Census
Bureau builds these estimates using the most recent census and administrative
data sources to estimate how the population has changed since the last census.
There is historical precedent for employing this method. The United States uses
the decennial census as a baseline against which population estimates areevaluated; any differences are considered flaws in the estimates and used to
improve those estimates in the future.²⁰ However, the difference between theestimates and the decennial census (the error of closure) is not only due to errors
in the estimates, it is also influenced by errors in the decennial census. In fact, in
Canada, analysis of the error of closure assumes that the errors occurred in thecensus, not the estimates, and corrections are made in the census based on theseanalyses.²¹ We suggest that looking at the potential for census errors using theerror of closure compensates for the deficiencies of only assuming error in theestimates.²² Both approaches are partially correct: Differences between theestimates and the decennial census are probably caused by errors in both data
sources. The error of closure (the difference between the census and theestimates) implies potential undercounts and overcounts in the decennial census
while recognizing that these differences could also be caused by errors in theestimates.

5

Introduction

Why the 2000, 2010,
and 2020 Censuses?
Every decennial census is different––
the technologies andmethodologies that the Census
Bureau uses to enumerate thepopulation can change
dramatically,²³ and ourunderstanding of how to measurecensus quality also evolves between
censuses.²⁴ We chose to compare
these three decennial censuses
because we want to understandboth recent historical trends as wellas impacts specific to the 2020
Census.²⁵ In addition, changes toThough our national analysis corroborates the PES insome decades and departs from it in others, there
are regional differences in potential net undercounts
and overcounts every decade. Our methodology, PA,
allows us to examine net overcounts andundercounts at the county level. We gain insight into
where counts were more accurate, where larger orsmaller overcounts and undercounts occurred, andexamine the impact of age on overcounts andundercounts. We dive into several case studies forboth Asian American and NHPI communities toillustrate how regional differences in these areas
may drive undercounts and overcounts. These casestudies provide insight into regional variations in
subgroup, citizenship status, and English language
ability.

race reporting in the 2000 Census

While our method provides valuable insight into

make it difficult to compare data

census quality for the Asian American and NHPIfrom earlier decades.²⁶

communities, it has limitations. We can provide

Declining trust in public sector
institutions, decreases in overall
response rate to all surveys andenumerations, and the increasing
costs of engaging the public torespond were problems before andduring the 2020 Census.²⁷ There
were also one-time events that
impacted the count, including thepoliticization of the decennial
census through the attempted
addition of a last-minute, untested
citizenship question on thedecennial form.²⁸ The
unprecedented COVID-19 pandemic
also impacted the count throughsome evidence, but we are not able to definitively
prove that an area had an undercount or overcountfor the Asian American or NHPI populations. We also
are not able to provide evidence of thesocioeconomic or demographic drivers ofundercounts if the data are not available in thedecennial census or population estimates. In termsof census quality, our method and sources do notallow for conclusions about subgroup identity,socioeconomic status, citizenship status, housing
arrangements, or other factors. This report’s casestudies delve deeper into some of these questions.
However, further research is clearly warranted. Insum, we cannot explain why undercounts orovercounts occur, but we can show when and where
they happen.

delays, and significantly changedsome operational processes.²⁹6

Introduction

Much more research is needed to further our understanding of decennial census
coverage. For example, in small communities, including the NHPI community inmany areas throughout the country, no single current method can provide
reliable measures of coverage. Further work is required to create more stableestimates of coverage for this and other relatively small communities.

Roadmap for the Report
We organize this paper into three parts. First, we outline the traditional methodsof census quality, DA and the PES, and explain their strengths and limitations
with respect to Asian American and NHPI communities. We also introduce ourmethodology, PA, and explain how it addresses the limitations of other methods.
In the second part of the paper, we use PA to analyze the quality of the census for
Asian American and NHPI communities, including four case studies for eachgroup. Finally, in the third part, we provide recommendations for the 2030
Census.

Key Takeaways
The Census Bureau continues to miss Asian Americans and NHPIs in thedecennial census. While these missing numbers have been offset by duplicate
responses at the national level, assessing census accuracy by race at lower
geographic levels illustrates that those missed and those overcounted are often
coming from different neighborhoods. As a result, those most likely to be missed
continue to lose out on their fair share of resources and political representation.
As planning for the 2030 Census moves forward, it is imperative thatpolicymakers, the Census Bureau, advocacy groups, and other interested parties
recognize that Asian Americans and NHPIs continue to risk being missed in thecensus and other surveys. Thus, they must plan for a more accurate count bymaking policy decisions and outreach plans that mitigate the risk of overcounts
and undercounts of Asian Americans and NHPIs. This includes, but is not limited
to, encouraging higher response rates and improving future measures of census
quality. Through this research, we seek to strengthen the census and thefoundational role it plays in our democracy.

7

Introduction

NOTES¹ https://www.census.gov/programs-surveys/decennial-census/about/why.html² https://gwipp.gwu.edu/sites/g/files/zaxdzs2181/f/downloads/Counting%20for%20Dollars%202020%20Brief%207A%20%20Comprehensive%20Accounting.pdf³ An example of how these differential undercount data have been used to improve decennial census operations isthrough creating a better understanding of the undercount of young children. E.g.,
https://www.census.gov/newsroom/blogs/director/2018/07/improving_our_count.html.
⁴ https://www.census.gov/programs-surveys/decennial-census/about/why.html
⁵ https://www.pewresearch.org/fact-tank/2021/04/09/asian-americans-are-the-fastest-growing-racial-or-ethnic-group-inthe-u-s/
⁶ Data based on authors' calculations from the 2000, 2010, and 2020 Censuses.
⁷ https://www2.census.gov/programs-surveys/decennial/2010/technical-documentation/complete-tech-docs/summaryfile/sf2.pdf, pgs. F44- F46.
⁸ https://www.advancingjustice-aajc.org/report/community-contrasts-asian-americans-united-states;
https://www.advancingjustice-aajc.org/report/community-contrasts-aanhpi-south;
https://www.advancingjustice-aajc.org/report/community-contrasts-aanhpi-west;
https://www.advancingjustice-aajc.org/sites/default/files/2016-09/A%20Community%20of%20Contrasts_Northeast.pdf;
https://www.advancingjustice-aajc.org/sites/default/files/2019-07/1153_AAJC_Immigration_Final_0.pdf;
https://archive.advancingjustice-la.org/sites/default/files/A_Community_Of_Contrasts_NHPI_CA_2014.pdf;
https://archive.advancingjustice-la.org/what-we-do/policy-and-research/demographic-research/community-contrastsnative-hawaiians-andpacific#:~:text=the%20United%20States-,A%20Community%20of%20Contrasts%3A%20Native%20Hawaiians%20and%20Pa
cific%20Islanders%20in,used%20in%20data%20collection%20; and;https://www.advancingjustice-chicago.org/wp-content/uploads/2015/08/Community_of_Contrasts_Midwest_2012.pdf
⁹ See the following for a discussion of the effectiveness of de-duplication in the 2010 Census:
https://www2.census.gov/programs-surveys/decennial/2010/program-management/5-review/cpex/2010-cpex-244.pdf.
¹⁰ https://www.advancingjustice-aajc.org/inside-the-numbers-report-2019¹¹ Erika Lee, The Making of Asian America: A History. New York: Simon & Schuster, 2015, p. 3.
¹² These are freely associated states that signed an agreement with the United States to allow a military presence in theircountries in exchange for a variety of benefits, including allowing residents to live and work in the United States without
applying for citizenship. See “Native Hawaiians & Pacific Islanders in the United States: A Community of Contrasts,” 2014,p. 21. Accessed at: https://archive.advancingjustice-la.org/sites/default/files/A_Community_of_Contrasts_NHPI_US_2014.pdf.
¹³ Ibid.
¹⁴ These unexpected differences could be due to many factors, but the populations in these areas tend to be more mobile,
more likely to be renters, and in some cases have a higher proportion of non-citizens.
¹⁵ See O’Hare, William P. Differential Undercounts in the U.S. Census Who Is Missed? 1st ed. 2019. Cham: Springer Nature,
2019, accessed at https://link.springer.com/book/10.1007/978-3-030-10973-8. For an understanding of what is and isn't
available for Asian Americans, see https://link.springer.com/chapter/10.1007/978-3-030-10973-8_9. For NHPIs, see:https://link.springer.com/chapter/10.1007/978-3-030-10973-8_11.

8Introduction (continued)

NOTES¹⁶ For the most recent Demographic Analysis coverage, see: https://www.census.gov/data/tables/2020/demo/popest/2020demographic-analysis-tables.html.
¹⁷ For the most recent PES results, see: https://www.census.gov/programs-surveys/decennial-census/about/coveragemeasurement/pes.html.
¹⁸ Another way to approach our research would be to look at how self-response differed throughout the country and to
extrapolate what that means for the Asian American and NHPI response rates in those areas. However, this would raisesimilar issues that we see in DA. Self-response rates are only available for the total population, so we would need to makesome large logical leaps (such as assuming that areas with high proportions of Asian American or NHPI respondents areindicative of all Asian Americans or NHPIs) to try and understand the impact of Asian American or NHPI response on thoseoverall rates.
¹⁹ I.e., in every year that does not end in zero.
²⁰ See, for example, the 2010 Estimates Evaluation: https://www.census.gov/content/dam/Census/library/workingpapers/2013/demo/POP-twps0100.pdf.
²¹ https://www150.statcan.gc.ca/n1/pub/91-214-x/2021001/section02-eng.htm²² We make comparisons without assuming one of the data sources is inherently better than the other, and approach the
problem with a focus on potential errors in the decennial census. This counterbalances the extensive work the Census
Bureau has done with the assumption that the population estimates are where error exists.
²³ https://www.census.gov/library/stories/2020/05/four-ways-new-technology-is-revolutionizing-the-2020census.html#:~:text=Now%20it%20relies%20much%20more%20on%20technology%20%E2%80%93,systems%20in%20our%
20%E2%80%98system%20of%20systems%2C%E2%80%99%E2%80%9D%20Colosi%20said²⁴ See, for example, the changes from post-stratification to logistic regression in the Post-Enumeration Survey between
2000 and 2010: https://www2.census.gov/programs-surveys/decennial/coverage-measurement/pes/estimation-methodsnet-coverage-estimation.pdf.
²⁵ The 2020 Census had multiple concerning issues, including, but not limited to, political interference and the COVID-19
pandemic. It was truly impressive that a decennial census was even completed, but concerns about the quality of thedecennial census were, and are still, a major issue. See, for example: https://www.pewresearch.org/facttank/2020/12/14/how-accurate-will-the-2020-u-s-census-be-well-know-moresoon/#:~:text=A%20good%20sign%20is%20that%20the%202020%20census,a%20neighbor%20or%20other%20%E2%80%9C
proxy%2C%E2%80%9D%20in%20census%20jargon; and https://thehill.com/opinion/congress-blog/3642800-safeguardingthe-census-bureau-from-politicization/.
²⁶ https://www.federalregister.gov/documents/1997/10/30/97-28653/revisions-to-the-standards-for-the-classification-offederal-data-on-race-and-ethnicity²⁷ https://hdsr.mitpress.mit.edu/pub/1g1cbvkv/release/8; and https://www2.census.gov/programssurveys/decennial/2020/program-management/planning-docs/2020-cost-estimate1.pdf²⁸ This action lead to multiple parties writing to Commerce Secretary Ross about the potential implications of thisaddition on the undercounts of immigrant communities. These groups included six former directors of the Census
Bureau, multiple academic and practitioner communities, groups of mayors, and advocacy organizations. For AAJC’s fact
sheet on the citizenship question, see:https://www.advancingjustice-aajc.org/sites/default/files/201804/AAJC%20Factsheet%20on%20Citizenship%20Question%20and%20Census%20REVISED%20April%202018.pdf.
²⁹ https://www.gao.gov/products/gao-21-206r

9

Measures of Census Quality
DA and the PES are the two widely accepted ways to measurecensus quality, but they are limited in their ability to explain

Calculating DA
The Census Bureau

the demographic and geographic variability of census quality.

calculates DA nationally

Demographic Analysis (DA)administrative data sources

Methodologically, DA is one of the strongest measures ofSpecifically, DA uses birth,

decennial census quality because it uses high-quality data

death, and Medicare records

sources and because of the near-independence of theseas well as estimates of net

measures from the decennial census.³¹ Near independence

international migration tomeans that DA does not rely on the census count or census

calculate population

counting process for its population estimate.³² Some form ofchanges. Birth and death

DA has been undertaken since the 1940 Census, and its

records, especially after 1945,

findings have been used to generally improve decennial

are a complete picture ofcensus overtime.³³

changes in the national

using highly reliable
about the population.

native-born population, andFigure 1

Net Over and Undercounts based on Demographic Analysis
U.S. Total Population: 1970-2020

Medicare records allow the
Census Bureau to capturechanges in the population

1%

that are not covered by birth
Error of Closure (%)

0.13

records for individuals born

0%

-2.71

-1.22

-1.65

before 1945. Finally, DA

-0.12

-0.30

-1%

includes an estimat﻿e of net
international migration overthe period using American
Community Survey data and-2%

foreign census data. This-3%

allows the Bureau to
1970
1970

1980
1980

1990
1990

2000
2000

2010
2010

2020
2020

Source: United States Census Bureau, Demographic Analysis, 1970-2020.

Figure 1 shows the net undercount or overcount for each decennial census since1970. Here, the bar’s deviation from zero indicates census quality––the further the
distance between the bar and zero, the worse the decennial census was. We seethat the overall trend is one of improvement. Since 1970, the net undercount has
shrunk by at least two percentage points.

10

estimate the number offoreign-born immigrants in
the United States as well as
the net movement ofAmerican expats living
overseas.³⁰

Measures of Census Quality

DA also provides some insight into data on coverage by race and data on coverageby age.
Figure 2

Net Over and Undercounts based on Demographic Analysis
U.S. Population, by Race Alone: 1970–2020
Black Alone

Non-Black Alone

2%

0.50

0.29
Error of Closure (%)

0%

-0.77

-1.08

-2.21
-2%

-2.30

-2.78
-4.50

-4%

-5.20

-5.52
-6%

0.42

-6.51

-8%
1970

1980

1990

2000

2010

2020

Source: United States Census Bureau, Demographic Analysis, 1970–2020.

Figure 2 shows coverage by DA race groups for each decennial census since 1970. The
undercount for the Black population has persisted over the decades, while the non-Black
coverage has changed from an undercount to a small overcount.

Data are provided for only two race groups³⁴ in DA: Black and non-Black (or allother races).³⁵ The data used to create DA cannot be disaggregated for racial
groups other than Black and non-Black because definitions of race have changed
over time.³⁶ Even from these limited race groups, trends emerge. The net
coverage for each group is different: The Black population has a significantly
larger undercount³⁷ than the rest of the population.³⁸ We do not know where
Asian Americans or NHPIs would fall in these undercount or overcount statistics
because they are part of the larger non-Black group. Our approach willdisentangle these differences in undercounts or overcounts for Asian Americans
and NHPIs.
DA also reveals differences in coverage by age: The youngest children are
persistently undercounted in the decennial census.³⁹ This observation holds truefor every decennial census in the United States and in many other countries aswell.⁴⁰ The reasons for this phenomenon are varied, but multiple research projects
have shown the culprits are some combination of complex household situations,missed households, and multiple missing members of households.⁴¹

11

Measures of Census Quality
Figure 3

Net Over and Undercounts based on Demographic Analysis
U.S. Population, by Single Year of Age: 2010–2020
2010

2020

10%

Error of Closure (%)

7.5%
5%
2.5%
overcount0%

undercount

-2.5%
-5%

0
3
6
9
12
15
18
21
24
27
30
33
36
39
42
45
48
51
54
57
60
63
66
69
72
75
78
81
84

-7.5%

Ages
Source: United States Census Bureau, Demographic Analysis, 2010–2020.

Figure 3 shows the net coverage by age from DA for the 2010 and 2020 Censuses. The
undercount of young children persists, while overcounts occur in both censuses for ages
18–22. Age heaping explains the spikes in the chart.

In 2020, age heaping––the tendency of people, especially when reporting age forothers, to round to the nearest 5 or 0––was a bigger problem than in 2010.⁴²
However, the systematic undercount of young children is a persistent problem
throughout the last two decades (at least). Older children, the college-aged oryoung adult population (ages 19–24), and older adults (such as those aged 65 andabove) are generally overcounted.
But what is the relationship between age and race for census response rates?
Figure 4 shows the net coverage based on DA for the Black Alone or in
Combination group as well as the non-Black Alone or in Combination groups
available from the 2010 DA. The undercount of young children is even more
pronounced in the Black population than the non-Black population.⁴³ Moreover,unlike the non-Black population, Black young adults and college students areundercounted.

Alone or Alone in Combination?
To capture the growing biracial and multiracial population, people can check more than
one of the available races when filling out the Census. The Census Bureau describes thisas either “Race Alone” or “Race Alone or in Combination.” Whenever we use Race Alone,
we mean those who only marked the specific race category. When we say Race Alone orin Combination, we mean anyone who marked that specific race category, regardless ofwhat other races they may have also selected.

12Measures of Census Quality
Figure 4

Net Over and Undercounts based on Demographic Analysis
U.S. Population, by Age and Race: 2010Black Alone or in Combination

Non-Black Alone or in Combination

8%

Error of Closure (%)

6%
4%
2%
overcount0%

undercount

-2%
-4%
-6%

0
3
6
9
12
15
18
21
24
27
30
33
36
39
42
45
48
51
54
57
60
63
66
69
72
75
78
81
84

-8%

Ages
Source: United States Census Bureau, Demographic Analysis, 2010–2020.

Figure 4 shows net coverage by DA race groups for the 2010 Census. Two things stand out:First, the undercount of young children exists for both race groups, but it is worse for theBlack population. Second, while young adults are overcounted for the non-Black population,
they are actually significantly undercounted for the Black population.

Although the undercount of young children persists across groups, it is worse for
the Black population. Some studies looking at early data from 2020 suggest theundercount for the young Black and Hispanic populations may have gotten worse
since 2010.⁴⁴ Unfortunately, we are not sure what this means for Asian Americans
and NHPIs because DA does not provide data on these populations. However, ourmethod helps to disentangle this data.

Demographic Analysis and the Asian American and NHPI Communities
DA provides no additional information about Asian American and NHPI
communities. Because DA only provides information for the Black and non-Black
populations, we cannot learn about the differential coverage of our populations ofinterest from these data.

13

Strengths

DA

The strength of DA lies in two areas: (1) near-complete independence
from the decennial census, and (2) the use of administrative data
sources that are known to be very high quality for counts and (some)demographic characteristics.⁴⁵ Because DA uses birth, death, Medicare
records, and estimates of net international migration to build out the
population by age, sex, and limited race groups, it is almost entirely
independent from the decennial census. It does not suffer frompotential correlation bias and errors with the decennial census that the
PES might.⁴⁶ The data that DA are built upon are of very high quality andcompletion. This means that any measures of net coverage based on DA
are also of very high quality.

Limitations

While DA is a solid measure of decennial census quality at the national level for selectrace and ethnicity groups and for age and sex,⁴⁷ its limitations include:
1. Lack of coverage at lower levels of geography: Because of the data sources used
and the difficulties of trying to measure domestic migration over the course ofpeoples' lives, DA is only available at the national level.
2. Lack of coverage of most race and ethnic groups: Because of the lack ofconsistent historical data for all race and ethnic groups, DA is only available for
the Black and non-Black populations as well as a subset of the Hispanic and nonHispanic populations (ages 0–29 in 2020 and ages 0–19 in 2010).⁴⁸
3. Net coverage: While other methods like the PES examine both net coverage aswell as its constituent components (duplicates and omissions), DA only provides
net overcounts or undercounts. This means that duplicates and omissions may
cancel themselves out, thereby obscuring the accuracy of the count.
Each of these limitations, especially regarding race, hampers efforts to better
understand coverage of Asian American and NHPI communities.

Follow-up QuestionsDA

The strengths and limitations of DA raise new questions. Do Asian American and NHPI
communities suffer the same undercount of young children that we see in the broader
population? Do we still see potential double counting of older children and college-aged
young adults? Is it possible to create at least a subset of Asian American and/or NHPI
estimates for DA?

14

Measures of Census Quality

Post-Enumeration Survey (PES)

Post-Enumeration
Survey Methodology

The Post-Enumeration Survey (PES) is another widely
accepted measure of decennial census quality. The PES reinterviews a subset of the population using a different source
for addresses than was used in the decennial census to see if

The Census Bureau conducts

to see if it successfully counted people “once, only once, and in

the PES—a survey of thethe right place.”⁵⁴ That is, the PES creates a full list of addresses

population—to better

within the blocks that they sample using different sourcing

understand coverage in theand methodology than the decennial census. This ensures that

decennial census. In 2020, thehousing units, and therefore people, were not missed in

Bureau went back and re-decennial census operations.

enumerated households in

Similar to DA, the PES reveals that the overall net overcount or
undercount has decreased over time. We can also examine thecomponents of the overall net undercount because the PES
measures each of these components instead of just net

which is a small fraction of the
5.8 million populated blocks
in the United States.⁵⁰ The
PES uses dual-systemcoverage. The 1990 undercount of over 1.5% turned into an
overcount in 2000 of nearly 0.5%. In 2010 we had nearly zeronet coverage error before seeing an undercount of

estimation (DSE)—a process
by which researchers at the
Census Bureau use two

approximately 0.2% in 2020.⁵⁵

different samples (apopulation, or P, sample andFigure 5

an enumeration, or E, sample)

Net Coverage Error from the Post-Enumeration Survey
U.S. Population: 1990-2020

to estimate the net coverage

0.5%

error and the components of
0.49

census coverage for each

0.01

0%

Error of Closure (%)

10,000 blocks in the US,⁴⁹

-0.24

-1.61

decennial census.⁵¹ To
oversimplify, Bureau-0.5%

researchers compare these
two samples with one another-1%

to identify those who werecorrectly, or incorrectly,-1.5%

enumerated in the decennial-2%

19901990

20002000

20102010

2020

2020

Source: United States Census Bureau, PostEnumeration Survey, 1990-2020.

Figure 5 illustrates the net coverage for the total population from the PES sincethe 1990 Census. The PES showed general improvement over time, as theabsolute value of the net coverage error decreased through 2010. In 2020, there
was an increase in coverage error.

15

census.⁵² A PES has been
undertaken in every decennial
census since 1950, and since1980 the PES has been
undertaken using DSE.⁵³

Measures of Census Quality
Figure 6

Components of Coverage from the Post-Enumeration Survey
U.S. Population: 2010, 2020
2010

2020

4%

3.4
2.8

Error of Closure (%)

2%

2.0

1.6
0.5 0.6-5.3 -5.8

0%Figure 6 reveals thecomponents of the net
overcount or undercount.
Specifically, we can lookat duplications, omissions,whole person
imputations, and othererroneous enumerations.

-2%

-4%

-6%
Erroneous
Enumerations(Duplication)

Erroneous
Enumerations(Other)Whole Person
Imputations

Omissions

Source: United States Census Bureau, Post-Enumeration Survey, 2010-2020.

But these net coverage numbers mask duplications and omissions that cancel
each other out.
The PES also provides state-level estimates for the total population and estimatesat the national level for all race and ethnicity groups.
Map 12020 Post-Enumeration Survey
U.S. Population
Net Coverage Error
undercountovercount

Map 1 shows the statelevel results of the PES
for the 2020 Census.
Most states haveestimates of coverage
error that are notstatistically significant.

Source: United States Census Bureau, Post-Enumeration Survey, 2020.

16

Measures of Census Quality

Thirteen states either had a statistically significant overcount or undercount in2020 (i.e., had a count that was statistically different from zero). Most of theundercounted states were in the South, including Texas, Arkansas, Mississippi,
Tennessee and Florida. The geographic outlier was Illinois. States with overcounts
included New York, Minnesota, Massachusetts, New Hampshire, Delaware, andUtah. Many of these states are in the Northeast or Upper Midwest, areas with ahistory of either net zero coverage error or slight overcounts.
The national-level data by race serve as a benchmark for PA comparisons for the
Asian American and NHPI race groups at the county and state levels. Different racegroups have different historical patterns in coverage. For instance, the Black
population has been undercounted in the past, while the white population has
traditionally been overcounted.⁵⁶ These differences impact political representation
and access to resources. There was a net overcount of Asian Americans in 2000, in2010 the coverage error was not statistically different from zero, and in 2020 there
was a net overcount. For the NHPI community there was a net undercount in 2000
and 2010, but a net overcount in 2020; however, none of these estimates were
statistically different from zero.
Figure 7

Net Coverage Error from the Post-Enumeration Survey
U.S. Population, by Race: 2010, 2020
2010

3%

2.6

Error of Closure (%)

2%

1.3
0.5 0.7

1%
0%

2020

0.0

-4.9

-0.2

-1%

-0.9

-1.2

-1.5

-2%

-2.1

Figure 7 shows the net
coverage by race /ethnicity for the past 2
censuses.

-3%
-3.3

-4%
-5%
Asian

NHPI

White

Black

American Indianand Alaska Native

Hispanic

Source: United States Census Bureau, Post-Enumeration Survey, 2010-2020.

The 2020 PES faced some unique challenges. Due to COVID-19, the 2020 PES wasseverely behind schedule.⁵⁷ Further, much like the decennial census and othersurveys that the Census Bureau conducts, response rates are declining. Because of
these issues, we believe the 2020 PES may not be as accurate as prior PESs.⁵⁸

17

Measures of Census Quality

Comparing the 2020 Census to other measures of census quality, such as DA andcomparing population estimates and the decennial census, are therefore all the

Limitations

PES

Strengths

more important.

The PES complements DA. Specifically, it provides a more granular look at
census coverage down to the state level. In prior decades, it even provided
coverage for large sub-state areas such as cities and counties withpopulations over 100,000.⁵⁹ It includes demographic characteristics, suchas race and housing tenure, that are not available in DA. Further, the PES
provides net coverage and components of coverage estimates, the latterof which is not available in DA. Put another way, through the PES, we candifferentiate the overall net overcount from the undercount by studying
the potential mitigating impacts of duplicates canceling out omissions.

The PES is a survey that uses a similar methodology to the decennial census. The
methodologies and collection methods are similar enough that the PES is more likely
to count or miss similar types of people, meaning it could replicate the same errors,such as omissions, as the decennial census. Thus, we may not see all errors in thedata. Second, the PES is only as good as its frame and the execution of the sample
survey.⁶⁰ This is especially significant given the badly delayed PES in 2020 where
there was more time between when people would have answered the decennial
census and the PES–likely decreasing respondents’ ability to recall their answers.⁶¹
Finally, while the PES provides estimates of coverage at lower levels of geography andestimates of coverage by race and ethnicity, it does not offer coverage estimates by
race and ethnicity at lower levels of geography. This can mask the variationin responses for different racial and ethnic groups throughout the country.

Follow-up QuestionsPES

The strengths and limitations of the PES raise new questions about how to improvedecennial census coverage generally and for Asian American and NHPI communities
specifically. Do these communities follow the same geographic patterns of duplications anderroneous enumerations as the total population? Or are the patterns for these communities
markedly different? That is, do we actually see areas in the United States where Asian
American and NHPI communities are significantly undercounted or overcounted, eventhough at the national level they are not? And what might account for these differences?
18

Measures of Census Quality

Post-Enumeration Surveys and the Asian American and NHPI Communities
Unlike DA, the PES can give us some detail about the quality of the decennial
census for Asian American and NHPI communities.
Figure 8

Net Coverage Error from the Post-Enumeration Survey
Asian American Community, U.S. Population: 1990-2020
3%

Error of Closure (%)

2%

2.62

1%

0.75

0.0

20002000

20102010

0%
-1%

-2.36

-2%
-3%

1990 1990

Figure 8 shows the net
coverage error for the
Asian American
community from 1990
to 2020. There has
been a shift away froman undercounttowards an overcount,and generally
decreasing net
coverage errors up
until 2010.

20202020

Source: United States Census Bureau, Post-Enumeration Survey, 1990-2020. Note: For 1990, Asian and NHPI was
still a combined category and therefore had the same estimated error.

The Asian American community was undercounted in the 1990 Census andovercounted in the 2020 Census, while the counts in 2000 and 2010 were notstatistically different from zero.⁶² However, improvements can be made on boththe undercount in 1990 and the overcount in 2020.
Figure 9

Net Coverage Error from the Post-Enumeration Survey
NHPI Community, U.S. Population: 1990-2020

1.5%

Error of Closure (%)

1%

1.28

0.5%
0%

-1.02

-0.5%
-1%

-2.36

-2.12

-1.5%
-2%
-2.5%

1990 1990

20002000

20102010

Source: United States Census Bureau, Post-Enumeration Survey, 1990-2020.

1920202020

Measures of Census Quality

NHPIs follow similar trends. In 1990, NHPIs were undercounted.⁶³ In 2000 and 2010,
the PES estimates are relatively large compared to other race groups, but they are
not statistically different from zero. Likewise in 2020, though the NHPI community
appears to have been overcounted in the PES, this count is not statisticallydifferent from zero. The small size of the population and the resulting small sample
sizes for the PES means that these relatively large PES estimates for NHPIs are notstatistically different from zero because the margins of error are so large. This could
denote that the NHPI count is correct; however, it could also signify that there is asignificant undercount. We simply cannot know whether the count is correct or if
there’s a significant undercount given the current large margins of error, basedon small sample sizes, of the PES for this group.
The PES also provides data for the components of net coverage error: Erroneous
Enumerations, Whole Person Imputations, and Omissions.⁶⁴ We examine thecomponents of coverage for Asian Americans and NHPIs in both 2010 and 2020.
Figure 10

Components of Coverage from the Post-Enumeration Survey
Asian American Community, U.S. Population: 2010, 2020
2010

2020

6%

Error of Closure (%)

5.3

4%

3.5

3.5

Whole Person
Imputation

Omissions

3.3
2.5
2.1
2%

Figure 10 shows
components ofcoverage from the PES
for the 2010 and 2020
Censuses. While net
coverage error wasnear zero in 2010 andthere was a more than
2% overcount in 2020,
there were actuallyhigher erroneous
enumerations andomissions in 2010 thanthere were in 2020.

0%

Erroneous
Enumerations

Source: United States Census Bureau, Post-Enumeration Survey, 2010-2020.

For Asian Americans, the net coverage in 2010 was not statistically different fromzero, while there was an overcount in 2020. But there were actually more omissions
and erroneous enumerations in 2010 compared to 2020, and there weresignificantly more whole person imputations in 2020 versus 2010.⁶⁵ Thus, there
were more “mistakes” or miscounts for Asian Americans in 2010 than in 2020.
However, more of these mistakes canceled each other out in 2010 than in 2020. This
made the net coverage of the Asian American population look better overall, eventhough there were actually more omissions and erroneous enumerations in 2010.

20

Measures of Census Quality
Figure 11

Components of Coverage from the Post-Enumeration Survey
NHPI Community, U.S. Population: 2010, 2020
2010

2020
7.9

8%

Error of Closure (%)

6.6

6%
4.2

4.2

4%

3.6
2.8

2%

0%
Erroneous
Enumerations

Whole Person
Imputations

Omissions

Source: United States Census Bureau, Post-Enumeration Survey, 2010-2020.

Figure 11 shows components of coverage from the PES for the 2010 and 2020
Censuses for the NHPI community. Erroneous enumerations stayed relatively
constant while omissions declined and whole person imputations increased
from 2010 to 2020.

For NHPIs, erroneous enumerations did not change significantly between 2010 and
2020. Omissions decreased slightly and whole person imputations increasedslightly. This likely means that fewer people were missed in 2020 than in 2010, andthat more of those missed were probably imputed into a correct status.
The PES at the State Level for the Total Population
For the total population, omissions and erroneous enumerations are not evenly
distributed throughout the United States. Data by race and by state does not exist
for the PES, precluding analysis of the distribution of omissions and erroneous
enumerations for Asian American and NHPI communities. It is highly unlikely thatthese erroneous enumerations and omissions would be the same for each state,much less each county.
Hawaii, Montana, New York, and Vermont have higher duplication rates while
Nevada, Idaho, and Virginia have lower rates (see Map 2). The reasons behind thesevariations are unknown, but some possibilities include greater seasonal or second
home populations, a greater proportion of college students, or a greater number ofpeople who move often.⁶⁶

21

Measures of Census Quality
Map 22020 Erroneous Enumerations (Duplications)
U.S. Population
Duplications (%)
0%

1%

3%

4%

6%

Map 2 shows erroneous
enumerations by state
for the total populationin 2020.
Source: United States Census Bureau, Post-Enumeration Survey, 2020.

Other Sources to Understand the Quality of the Decennial Census
Other sources can augment
existing measures ofdecennial census quality.
Academics and nonprofit
organizations conduct
census quality analysis (bothduring and after thecollection of decennial
census data) to identify
potential areas of concern or
future improvement.⁶⁷
For example, after the 2020
Census, the National
Academy of Sciences worked
inside the Census Bureau toget access to operational
metrics, allowing them tobetter assess the quality ofthe decennial census.⁶⁸

But what exactly is anoperational metric? Operational
metrics are data the Census
Bureau collected and usedduring the fielding of thecensus to make sure that thecount was as complete as
possible.⁶⁹ They include thenumber of households who
self-respond in a census tract or
the percentage of households
in a census tract who utilized
proxy responses during
Nonresponse Followup. When
the Census Bureau releases
these data to the public, we canmore effectively evaluate theaccuracy and reliability of the
final census results.

22

Assessing these operational
metrics reveal geographies
where the census count
may have been of higher orlower quality. These
analyses indicate whether
the 2020 Census was
generally fit for use.
However, operational
metrics are unlikely to help
evaluate the quality of thedecennial census for Asian
American and NHPI
communities because thesedata are not reported bydemographic detail.

Measures of Census Quality
Map 32020 Omissions
U.S. Population
Omissions (%)
0%

3% 5%

8%

11%

Map 3 shows the state
estimates of omissions
for the total population
for the 2020 Census.
Omissions were thehighest in the Southeast
and Montana.
Source: United States Census Bureau, Post-Enumeration Survey, 2020.

There are strong similarities between the map for omissions (Map 3) and theoverall net coverage map (Map 1) for the entire population; Arkansas, Mississippi,
Texas, and Florida, all had a high percentage of omissions and undercounts.
Overall, there are larger numbers of omissions in the South, especially in theSoutheast, and fewer omissions in the Northeast, Upper Midwest, Northwest andparts of the Mountain West. We suspect that the differing rates of omission could
be explained partially by Get Out the Count (GOTC) efforts. Factors that impact
GOTC efforts might include: the proportion of hard to count communities relative
to the investment, who made the investment, and to whom they were made.⁷⁰
For Asian American and NHPI communities, there is near zero net coverage error
at the national level because duplications and omissions largely cancel each otherout. However, for the NHPI community, sample sizes are too small to makeconclusions about whether the non-statistically significant findings are real, or ifthey are due to the large margins of error in these measurements. Furthermore,
the absence of state-level data by race prohibits us from capturing informationfrom the current PES below the national level.

23

Measures of Census Quality

Importance of self-response rates for Asian
Americans and NHPIs in the census
Self-responses are considered the highest quality data in a census because they area household's submission of their own census form.⁷¹ Additionally, “past research[has shown] that the lower the self-response rate is, the higher the risk of a net
undercount for a given subpopulation group.”⁷² For these reasons, securing thehighest level of self-response during a census is a critical goal–even though selfresponse is not an official measure of census quality.
Self-response rates are not evenly spread across different communities and many
variables can influence the ability of particular groups to self-respond. Because selfresponse rates are available down to the Census Tract but not by race, ethnicity, or
any other demographic characteristics, it makes them harder to interpret fordifferent populations. More research is needed to deepen our understanding not
only of a community's self-response rates, but also the variables that negatively
impact the self-response rates. This will inform future outreach efforts to ensure
effective targeting and increased self-response.

A New Approach––Population Estimates Analysis (PA):Comparing Population Estimates and the Decennial Census
While DA and PES can help identify some strengths and weaknesses of a census,
they cannot tell us much about Asian American and NHPI communities. Our
alternative, PA, reveals potential undercounts and overcounts at lower levels ofgeography in the decennial census for Asian American and NHPI communities. By
comparing the decennial census to postcensal population estimates released by
the Census Bureau, we capture potential undercounts or overcounts for Asian
American and NHPI communities.

The Postcensal Population Estimates
The Census Bureau’s postcensal population estimates are the official population
numbers for the United States in every year not ending in zero. To calculate thisvalue, the Census Bureau starts with the most recent decennial census and thenadds or subtracts population based on information from several administrative
record sources. From this, they develop an estimate of the population in a given
year.⁷³

24

Measures of Census Quality

Population estimates are used, directly and indirectly, to distribute state andfederal funding and as weights for all population-based surveys.⁷⁴ Policymakers,
academics, state and local governments, advocacy groups, businesses, and manyothers use population estimates, even if only through their indirect impact on theAmerican Community Survey.⁷⁵ Figure 12 outlines how population estimates are
created and details our approach–comparing the population estimates directly tothe decennial census.
Figure 12

Census Quality: Population Estimates Analysis
Population Estimates
IRS

Previous
CensusBirthsDeathsBirthsDeathsMedic

Comparison

areDomestic
Migration

Immigration

Group
Quarters

Population
Estimate

Current
CensusOne of the strengths of PA is that many of the same data sources used in DA are
also employed in the population estimates. However, there are two significant
differences between DA and the postcensal population estimates. First, DA is
based on a time series of administrative data going back to 1945. In contrast, thepopulation estimates (the basis for PA) use the last decennial census as thestarting point for its calculations. From there, administrative data from that dateforward are applied to measure the change in the population. Second, DA is only
available at the national level. Because DA is only at the national level, it does notaddress domestic migration—one of the hardest components to correctly estimate—in the same way that the population estimates do.
To capture the potential undercounts and overcounts of Asian American and NHPI
communities, we focus on the error of closure.⁷⁶ The error of closure is thedifference between the population estimates and the decennial census as ofcensus day (April 1 of 2000, 2010, and 2020). Specifically, we subtract the population
estimates value from the decennial census to get a raw potential undercount orovercount. For this calculation, we use the evaluation estimates series produced by
the Census Bureau. These data start with the prior decennial census and provide
an estimate for every year between that date and the current census. They also
provide an estimate for the current decennial census date, which is what we use
for PA.

25

Measures of Census Quality

A note on race...
Defining, classifying, and calculating race has changed over time.⁷⁷ Here, we focus on a numberof topics that have distinct impacts on the decennial census and on other products such as the
population estimates.
Current race and ethnicity questions: When filling out the decennial census, people can check and write inany of the race categories on the following form, or any combination thereof (Question 9). Separately, each
person also fills out Question 8 about Hispanic origin.
The ability to select more than one race when filling out the census: Beginning with the 2000 Census,
people could check more than one of the available races.⁷⁸ This improvement allowed the decennial
census to capture the growing biracial and multiracial population, but it also created complications.⁷⁹ For
instance, the Asian American community could refer to only those individuals who selected an Asiancheckbox, or it could capture people who selected an Asian checkbox along with other races. The Census
Bureau describes this as either “Race Alone” or “Race Alone or in Combination.” Whenever we use Race
Alone, we mean those who only marked the specific race category. When we say Race Alone or in
Combination, we mean anyone who marked that specific race category, regardless of what other races
they may have also selected.
The ability to report "Some Other Race" (SOR): In 2000, for the first time, people were allowed to selectSOR, either alone or in combination with other races.⁸⁰ This impacts our analysis because for both DA and
the PES, SOR is not reported.⁸¹ Instead, the Census Bureau must create a modified race file that recodes
everyone who responded SOR into one of the five race categories (or combinations thereof). These
modified race data are often released around the same time that detailed data are released from thedecennial census. However, since many data products for the 2020 Census have been delayed, we created
our own modified race file (see Appendix 1 for a discussion on methodology for this file).⁸² Throughout thereport, we will discuss only our modified race file, not the raw decennial census data that include SOR.
Changes in race categories: Another change made in 2000 was the ability to select Asian American or NHPI
as separate race groups. This was the result of the requirements set out in the revised 1997 Office ofManagement and Budget (OMB) standards on race and ethnicity.⁸³ Before 2000, this was a combinedgroup that the Census Bureau called “Asian or Pacific Islander” (API).⁸⁴ Our 2000 analysis compared thegroups in a combined form because the postcensal population estimates available for April 1, 2000 are
based off of the 1990 Census. Therefore, they only include data on these two communities combined. Allanalyses of the 2010 and 2020 censuses split the groups into their own categories.
Changes in internal Census Bureau processes to code race and ethnicity: For each decennial census, theCensus Bureau makes rules about how to code each respondent’s answer to the race question on thecensus form.⁸⁵ In 2020, for the first time, the Census Bureau added an option to write in origin or race
information below the checkbox for all races. They created new rules to facilitate more inclusive
determinations of intended race based on those write-ins, and accepted more characters than they have inthe past for those write-ins.⁸⁶ As a result, in the 2020 Census, more people reported being multiracial. This isnoteworthy because the 2020 postcensal population estimates, which are based on the 2010 census, werecoded another way. There may be a lower representation of “in combination” race reporting compared to
the 2020 Census because the Census Bureau asked the race question differently in 2010. While there is not
yet a way to see the overall impact of this change on race reporting, multiple race reporting did increasesignificantly in 2020.⁸⁷
26

Measures of Census Quality

Source: 2020 Census Informational Questionnaire27

Measures of Census Quality

Like the DA and the PES, using the postcensal population estimate through PA toevaluate coverage for Asian American and NHPI communities has strengths andlimitations.
The Strengths and Limitations of PA
Generally, when comparing two different data sets to ascertain the quality of oneof the data sources, it is important to pinpoint the source of error and identify how
much we can say about the quality of the data source. This is the case for PA (seeFigure 13), as well as for DA or the PES. For PA, since we use the Error of Closure asa measure of potential net coverage error, error can come from one of threepotential places.
Figure 13

Population Estimates Analysis (PA)the what and why

What? The error of closure is another way to
say the difference between population
estimates and the decennial census as ofcensus day (April 1 of 2000, 2010, and 2020).

Why? Knowing the direction of these differences
can potentially help us to understand the quality ofthe decennial census. However, the differences canactually be due to three different errors:
The first error is the one we are interested inunderstanding: the error in the current decennial
census.

Error of
ClosureDecennial
Census

The second error can be the base that thepostcensal estimates are built off of (i.e., the lastdecennial census).

Population
Estimates

The third error could be found in the data or
methods that are used to estimate change
between the prior and the current census.

Historically, most analysis of the error of closure in the United States aims tounderstand errors in the base (2) or errors in the data or methods (3). As thepostcensal population estimates have improved over the decades, they have becomea better indicator of potential undercounts and overcounts––therefore helping us tobetter understand the error in the current decennial census (1).

28

Measures of Census Quality

Specifically the errors are: (1) the error in the current decennial census, which iswhere we want to focus. The second and third errors are errors in the population
estimates that are being compared to the decennial census. These errors comefrom (2) the base that the postcensal estimates are built upon (i.e., the lastdecennial census) or (3) mistakes in the components of change that the postcensal
estimates are built on (i.e., the births, deaths, and migration estimates between thelast census and the current census). Historically, most analyses that looked at theerror of closure have focused on errors (2) and (3).⁸⁸ However, errors from (1) exist.
As the postcensal population estimates have improved, they have becomesuperior (but still flawed) indicators of potential census undercounts andovercounts. Errors in the population estimates and errors in the decennial census
may still contribute to the error of closure. Despite these potential coverage issues,
PA pushes the field forward to better measure coverage for all groups.
A strength of PA is that it produces potential undercounts and overcounts down tothe county level by race. This overcomes one of the main limitations of DA and the
PES. However, PA also has limitations. Estimating the population at these lower
levels of geography by race, as well as the use of domestic migration, increases
opportunities for error. Because the source of the error (either the decennial
census or population estimates) is unknown, we discuss potential overcounts andundercounts using PA, focusing on trends rather than specific measures. This issue
becomes more pronounced for smaller groups. Therefore, we urge further caution
when discussing NHPI results.
Figure 14 summarizes the key pros, cons, and impacts of each approach formeasuring census quality discussed in this paper.

29

Measures of Census Quality
Figure 14

Understanding Census Quality
Figure v

Demographic Analysis (DA)Near-complete independence from decennial census
Uses high quality administrative data sources

Strengths
Limitations

Lack of coverage below the national level
Lack of coverage of most race groups
Can only look at net coverage
No additional information about Asian American and NHPI communities

ImpactPost-Enumeration Survey (PES)Strengths
Relatively independent from the decennial census
More granular look at census coverage by state and demographic characteristics
Helps identify gross coverage
Potential for correlated errors and bias
Only as good as the frame and execution of the sample survey
Lacks estimates of coverage by race and ethnicity at the subnational level
Some additional information about Asian American and NHPI communities
Shows relatively good net undercount and overcount numbers for Asian
American community, along with NHPI community (though the margins oferror for NHPI are quite high)Though net numbers are near zero, still significant duplications and omissions
that require further understanding and study

Limitations

ImpactPopulation Estimates Analysis (PA): Our Approach
Identify potential undercounts and overcounts at the county level by race,and for all race groups by age
Strong for identifying patterns that will signal areas for future research
Higher error than DA
Unknown if error comes from the current census, prior census, orcomponents of change

Strengths

Limitations

ImpactCoverage for the Asian American and NHPI communities varies throughout thecountry
Differences for the NHPI community are large, showing the difficulties of coverage
analysis for smaller racial groups, requiring more investment
Coverage for the Asian American and NHPI communities varies by age, and likely
subgroup (though this is harder to support with available data– see case studies)30

Measures of Census Quality

NOTES³⁰ https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020da_methodology.pdf³¹ See, O’Hare, 2019: https://link.springer.com/book/10.1007/978-3-030-10973-8.
³² One could argue that the Demographic Analysis is indirectly influenced by the decennial census counts because of
their use of the American Community Survey to estimate net international migration. However, the Census Bureau has
undertaken several sensitivity analyses to ensure there is limited impact of this codependence.
³³ That doesn’t mean the road has always been linear. In fact, we see that the 1990 Census had a larger undercount than in
1980, and 2020 has a larger undercount than 2010’s overcount.
³⁴ See, for example: https://www2.census.gov/programs-surveys/popest/technicaldocumentation/methodology/2020da_methodology.pdf.
³⁵ Though not pictured here, for 2010 and 2020 we can look at a subset of Hispanic and non-Hispanic for ages 0–29 in 2020
and ages 0–19 in 2010.
³⁶ It is technically possible to define DA for the white population because of the historical availability of data with “white”
as a defined race, but the Census Bureau does not currently do this.
³⁷ See, for example: https://link.springer.com/chapter/10.1007/978-3-030-10973-8_8.
³⁸ We also see in the analysis of the Hispanic population that, for the ages 0–29 at least, they are undercounted morewhen compared to the non-Hispanic population of the same age group.
³⁹ Numerous papers exist on this topic. For one of the more recent, see:https://www.census.gov/library/stories/2022/03/despite-efforts-census-undercount-of-young-children-persists.html; andhttps://2hj858.a2cdn1.secureserver.net/wp-content/uploads/2022/03/CAK-Report-on-Release-of-PES-and-DA-data-March10-2022-FINAL-3-10-2022.pdf.
⁴⁰ See O’Hare, 2017: https://content.iospress.com/download/statistical-journal-of-the-iaos/sji1008?id=statistical-journal-ofthe-iaos%2Fsji1008.
⁴¹ https://www2.census.gov/programs-surveys/decennial/2020/program-management/final-analysis-reports/2020-report2010-undercount-children-summary-recent-research.pdf
⁴² For a discussion on age heaping in the 2020 Census, see: https://www.census.gov/library/stories/2022/03/who-wasundercounted-overcounted-in-2020-census.html.
⁴³ This is also the case for the Hispanic population.
⁴⁴ We will not know the impact on young children by race for DA in 2020 until the Census Bureau releases single year ofage by race/ethnicity data for the 2020 Census as well as a modified race file from the 2020 Census. Both of these releases
should happen sometime in 2023: https://www2.census.gov/programs-surveys/decennial/2020/programmanagement/DHC%20Timeline. See also: https://www.documentcloud.org/documents/21097003-constance-citropreliminary-assessment-of-aspect-of-2020-census-quality-via-demographic-analysis-oct-16-2021-version (Slide 10 and p. 9of the report).
⁴⁵ See Footnote 31.
⁴⁶ See, for example: https://www2.census.gov/about/partners/cac/sac/meetings/2021-03/discussant-post-enumerationsurvey-update.pdf for a discussion of how DA is used to adjust the PES to deal with correlation bias.
⁴⁷ For coverage of the limitations of DA, see O’Hare 2019: https://link.springer.com/chapter/10.1007/978-3-030-10973-8_3.
⁴⁸ Hispanic and non-Hispanic analyses were done for ages 0–29 in 2020 and ages 0–19 in 2010.
⁴⁹ The PES has two samples, so for these 10,000 blocks the sample was approximately 160,000 households in one sample
and an overlapping 180,000 households in the other.

31

Measures of Census Quality (continued)

NOTES
⁵⁰ https://georgetown.app.box.com/s/0b4upgbombi0s8jf17pvibyk8v4nf7in
⁵¹ The P sample is a sample of households where the PES is undertaken completely separately from the decennial census.
The E sample is a universe of households enumerated in the same areas as the P sample during decennial census
operations.
⁵² For further discussion of the 2020 PES, see the series of papers by the Census Bureau released under “Reports”:
https://www.census.gov/programs-surveys/decennial-census/about/coverage-measurement/pes.html.
⁵³ https://www2.census.gov/programs-surveys/decennial/coverage-measurement/pes/estimation-methods-net-coverageestimation.pdf
⁵⁴ https://www.census.gov/programs-surveys/decennial-census/about/why.html
⁵⁵ We use the PES data for 1990 forward (instead of 1970 forward like DA) because of ease of data availability.
⁵⁶ See O’Hare, 2019 for coverage by different race groups in the PES: https://link.springer.com/book/10.1007/978-3-03010973-8.
⁵⁷ https://www.federalregister.gov/documents/2020/07/13/2020-14977/notice-of-correction-2020-census-postenumeration-survey-initial-and-final-housing-unit-followup#:~:text=The%20U.S.%20Census%20Bureau%20is%20issuing%20this%20notice,17%2C%202019%20%28Vol.%2084%2C%2
0No.%2074%2C%20pp.%2016000-16002%29
⁵⁸ For a more in depth discussion of the potential issues in the 2020 PES, leading to our assessment that it may have moreaccuracy issues than prior PESs, see: https://www.gao.gov/products/gao-22-105324.
⁵⁹ In 2020 these sub-state data were not released by the Census Bureau, though they have not published a reasoning
behind this decision. See https://www.census.gov/newsroom/pdf/20120512_ccm_newsconf_slides.pdf p. 28.
⁶⁰ The frame, or sampling frame, is the list of addresses from which the Census Bureau pulls its samples of homes tosurvey.
⁶¹ The PES asks basically the same questions as the decennial census, including using the same reference date.
⁶² In 1990, NHPI and Asian American data were still collected in one combined race group, leading to the same net
undercount for both groups.
⁶³ In 1990, NHPI and Asian American data were still collected in one combined race group, leading to the same net
undercount for both groups.
⁶⁴ Here we have collapsed Erroneous Enumerations (Duplicates) and Erroneous Enumerations (Other) for ease ofinterpretation.
⁶⁵ There were also significantly more whole person imputations in 2020 versus 2010.
⁶⁶ https://www.census.gov/newsroom/blogs/random-samplings/2021/04/how_we_unduplicated.html
⁶⁷ See, for example: https://www.censushardtocountmaps2020.us/ and https://mccourt.georgetown.edu/ccat/.
⁶⁸ https://nap.nationalacademies.org/catalog/26529/understanding-the-quality-of-the-2020-census-interim-report
⁶⁹ https://www.census.gov/newsroom/press-releases/2022/2020-census-operational-quality-metrics.html
⁷⁰ Trying to draw causal inferences on the impact of GOTC efforts is a tough business. However, a few groups have done
great work to try and look at these impacts, including: https://funderscommittee.org/2020censusnationalevaluation/andhttps://www.gcir.org/sites/default/files/resources/CA%20Census%20Statewide%20Funders%27%20Initiative%20Evaluation%20Report.pdf.

32

Measures of Census Quality (continued)

NOTES
⁷¹ See, https://www.census.gov/newsroom/blogs/random-samplings/2021/04/examining-operational-metrics.html;
https://www.census.gov/newsroom/blogs/random-samplings/2021/05/2020-census-operational-quality-metrics-release2.html;
https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.17409713.2020.01356.x#:~:text=Self%2Dresponse%20is%20the%20gold%20standard%20of%20data%20collection%20in,data%20
and%20the%20overall%20count;https://www.nyc.gov/assets/planning/download/pdf/planning-level/nyc-population/census2020/importance-selfresponse.pdf;
https://www.census.gov/programs-surveys/decennial-census/decade/2020/planning-management/plan/selfresponse.html.
⁷² https://www.urban.org/sites/default/files/publication/103306/risk-factors-affecting-the-fairness-and-accuracy-of-the2020-census-rob-santos.pdf
⁷³ This population is as of July 1 of the year the Census Bureau is estimating.
⁷⁴ See, for example:
https://gwipp.gwu.edu/sites/g/files/zaxdzs2181/f/downloads/Counting%20for%20Dollars%202020%2008-22-17_0.pdf.
⁷⁵ For information on the impact of the estimates on the ACS, see: https://www2.census.gov/programssurveys/acs/methodology/design_and_methodology/2022/acs_design_methodology_ch11_2022.pdf.
⁷⁶ According to the Census Bureau, error of closure is “the difference between the April 1 postcensal estimate and April 1
census count for the end of the decade.” See: https://www.census.gov/programs-surveys/popest/guidance.html.
⁷⁷ Scholarship on U.S. census history that discusses racial formation includes Anderson, Margo J. The American Census: aSocial History. Second edition. New Haven: Yale University Press, 2015; Bouk, Dan. Democracy’s Data: the Hidden Stories in
the U.S. Census and How to Read Them. First edition. New York: MCD, 2022; Hochschild, Jennifer L., and Brenna Marea
Powell. “Racial Reorganization and the United States Census 1850–1930: Mulattoes, Half-Breeds, Mixed Parentage,
Hindoos, and the Mexican Race.” Studies in American Political Development 22, no. 1 (2008): 59–96; Mora, G. Cristina.
Making Hispanics: How Activists, Bureaucrats, and Media Constructed a New American. Chicago ; The University of
Chicago Press, 2014; Nobles, Melissa. Shades of Citizenship : Race and the Census in Modern Politics. Stanford, CA:
Stanford University Press, 2000; Prewitt, Kenneth. What Is “Your” Race? : The Census and Our Flawed Efforts to Classify
Americans. Princeton, NJ: Princeton University Press, 2013; “The Census Counts, the Census Classifies,” in Foner, Nancy,and George M. Fredrickson, eds.. Not Just Black and White : Historical and Contemporary Perspectives on Immigration,
Race, and Ethnicity in the United States. New York: Russell Sage Foundation, 2004; Schor, Paul. Counting Americans :How the US Census Classified the Nation. New York, NY: Oxford University Press, 2017; Thompson, Debra. The SchematicState : Race, Transnationalism, and the Politics of the Census. Cambridge, United Kingdom: Cambridge University Press,
2016.
⁷⁸ https://www.federalregister.gov/documents/1997/10/30/97-28653/revisions-to-the-standards-for-the-classification-offederal-data-on-race-and-ethnicity
⁷⁹ E.g., https://www.pewresearch.org/social-trends/2015/06/11/chapter-1-race-and-multiracial-americans-in-the-u-s-census/;
https://www.census.gov/library/stories/2021/08/improved-race-ethnicity-measures-reveal-united-states-population-muchmore-multiracial.html; andhttps://www.census.gov/newsroom/stories/multiracial-heritageweek.html#:~:text=The%202020%20Census%20shows%20the,Other%20Race%20(1%20million).
⁸⁰ https://www.federalregister.gov/documents/1997/10/30/97-28653/revisions-to-the-standards-for-the-classification-offederal-data-on-race-and-ethnicity;
https://www.theatlantic.com/politics/archive/2016/08/the-rise-of-the-others/497690/

33

Measures of Census Quality (continued)

NOTES
⁸¹ In the 2010 Census, in producing the Modified Age, Race, and Sex (MARS) File, the Census Bureau recoded over 485,000
people who were "Some Other Race Alone" to "Asian," and nearly 135,000 to "NHPI." See
https://www.census.gov/data/datasets/2010/demo/popest/modified-race-data-2010.html for more details.
⁸² Delays in releasing these data for the 2020 Census occurred because of the COVID-19 pandemic, the politicization of the2020 Census, and issues with the Census Bureau implementing the new Disclosure Avoidance System for the 2020
Census.
⁸³ https://www.federalregister.gov/documents/1997/10/30/97-28653/revisions-to-the-standards-for-the-classification-offederal-data-on-race-and-ethnicity
⁸⁴ We use the term AANHPI when we talk about these two groups combined.
⁸⁵ For more information on these changes, see: https://www.census.gov/library/stories/2021/08/improved-race-ethnicitymeasures-reveal-united-states-population-much-more-multiracial.html;
https://www2.census.gov/about/cic/Coding%20Operations%20of%20Race%20and%20Ethnicity.pdf.
⁸⁶ The write-ins character capture went from 30 characters in 2010 to 200 characters in 2020. Further, the Census Bureau
coded up to six race or ethnicity responses versus two in 2010 from these write-ins. For further examples see:https://www.census.gov/newsroom/blogs/random-samplings/2021/08/improvements-to-2020-census-race-hispanicorigin-question-designs.html.
⁸⁷ Two things will be required to make these comparisons: First, we need a final modified race file for the 2020 Census.
Second, the Census Bureau needs to conduct research to show what the data would look like if they had not made theirchanges.
⁸⁸ See, for example: https://www.census.gov/content/dam/Census/library/working-papers/2013/demo/POP-twps0100.pdf.

34

Analysis: The Quality of Census Counts for
Asian American and NHPI Communities

Our analysis compares data from the decennial census with postcensal population
estimates for the 2000, 2010, and 2020 Censuses to provide us with better, moregranular information about the accuracy of our communities’ census counts.
Because Asian Americans and NHPIs are not a monolith, we disaggregate census
quality data for Asian Americans and NHPIs where such data are available.⁸⁹

Analysis: The Quality of the 2000 Census for Asian American and
NHPI Communities
For the first time in 2000, the census split the race category “Asian and PacificIslander” into “Asian” and “Native Hawaiian and Other Pacific Islander.”Respondents were also allowed to check multiple races on their census form.
Because of these changes, we start our PA analysis with the 2000 Census.
However, because the 2000 population estimates are built off the 1990 Census,
these race categories are still combined into one group in the 2000 population
estimates. Therefore, we compare the postcensal population estimates to those
who responded Asian and/or Pacific Islander Alone in the 2000 Census by creating
an AANHPI group.
At the national level, the 2000 Census came in 1.27% below PA estimates for the
API Alone race group, potentially resulting in a slight undercount. However, thesame census came in 14.31% above PA estimates for the API Alone or in
Combination race group––a relatively large overcount. This difference in coverage
is not surprising given that we are comparing different things. The population
estimates we are using for this analysis are based on a system that only allows
respondents to choose one race. If someone views themselves as only one race,
either Asian American or NHPI, this is not an issue. However, if someone views
themselves as having multiple races, but was forced to only choose one, it isinevitable that some of these people would have chosen a race other than Asian
American or NHPI. We would expect the population estimates to capture morethan the Asian American Alone or NHPI Alone respondents in the 2000 Census
because some people who were multiple races in 2000 would have marked only
AANHPI in 1990. Likewise, we would not expect the population estimates to35

Analysis: Asian American and NHPI Communities––2000 Census

capture all of the Alone or in Combination respondents in the 2000 Census. This isbecause some of the people who marked Asian American or NHPI as one of theirmultiple races may not have selected Asian American or NHPI if they were only
allowed to choose one race.
These differences between Alone and Alone or in Combination were not spread
evenly by state.
State-level variation exists with respect to potential overcounts and undercounts for
Asian Americans and NHPIs.⁹⁰ The AANHPI Alone population has more undercounts
in the West than in the East (see Map 4), but the difference between East and Westdisappears when we look at AANHPI Alone or in Combination (see Map 5).⁹¹
Map 4

Map 5

Percent Difference between 2000 Census Raceand Population Estimates, by StateAANHPI, Alone Population

Percent Difference between 2000 Census Raceand Population Estimates, by StateAANHPI, Alone or in Combination Population

Error of Closure (%)

Error of Closure (%)-25% 0% 25% 50%-25% 0% 25% 50%

Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimates data for 2000. Note: An error of closure
value less than 0% indicates a potential undercount, i.e. the population estimate for API (Alone) was less than census results.

Map 4 illustrates the percent differences for the AANHPI Alone group between the 2000 Census and the 2000
Population Estimates for the AANHPI Alone community per state. Map 5 shows the same information for the
AANHPI Alone or in Combination per state.

What do the relationships between race and net coverage error look like at thecounty level? While PA revealed a slight potential undercount for the AANHPI Alone
community nationally in the 2000 Census, the AANHPI community was notnecessarily undercounted everywhere.
36Analysis: Asian American and NHPI Communities––2000 Census

Figure 152000 Census Accuracy: AANHPI Alone

43%

55%

2%

of counties
overcountedof counties
undercountedof counties
no difference

Figure 15 is a gauge chart that shows thepercentage of counties that either had apotential overcount, undercount, or nodifference for the AANHPI Alone
population in our comparison of the
population estimates and the 2000 Census.

Source: Authors' calculations using United States Census Bureau decennial census
and postcensal population estimates data for 2010 and 2020.

Indeed, overcounts existed in 43% of counties. It is probable that the low overall net
coverage error for both the Asian American and NHPI communities in the 2000
Census was not evenly distributed geographically. Instead, overcounts in someplaces likely canceled out undercounts in others, leading to a national count thatappeared accurate.

Figure 16

Figure 17

Percent Difference between 2000 Census Race andPopulation Estimates of Counties: AANHPI Alone

Percent Difference between 2000 Census Race andPopulation Estimates of Counties: AANHPI Alone orin Combination

overcount

1,500

overcount

1,500

undercount or
no difference

undercount or
no difference
1,000Counties

500

500

Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimates data for 2000.

Figures 16 and 17 show how many counties had a potential overcount or undercount for the AANHPI Alone(Figure 16) or AANHPI Alone or in Combination (Figure 17) populations. They also illustrate the size of theundercount or overcount. For example, Figure 17 shows that over 500 counties had a potential overcount ofover 50%, and dozens had potential overcounts over 100%. In the context of a potential national undercountof just over 1%, it is probable that the low overall net coverage error for both the Asian American and NHPI
communities in the 2000 Census was not evenly distributed geographically.

37

15
0

th
an

-1
50

-1
00

10
1

M
or
e

Percent

51

Le
ss
th
an
-1
50
-(1
00
-1
49
)
-(5
0
-9
9)
-(4
9)
-0

15
0

M
or
e

th
an

-1
50

-1
00

10
1

-5
0

51

1

Le
ss
th
an
-1
50
-(1
00
-1
49
)
-(5
0
-9
9)
-(4
9)
-0

Percent

-5
0

0

0

1

Counties

1,000Analysis: Asian American Community––2010 and 2020 Censuses

Analysis: The Quality of the Census for the Asian American
Community for the 2010 and 2020 Censuses
We can compare population estimates and the decennial census for the Asian
American and NHPI communities separately for the 2010 and 2020 Censuses.⁹² We
focus first on the Asian American community, comparing the Asian Alone category
and the Asian Alone or in Combination category.
National Net Coverage Error by Race Category: PA Method

Table 1

2010

2020

Non-Hispanic Population

Total Population

Non-Hispanic Population

Asian Alone

4.6%

5.6%

2.5%

Asian Alone or in
Combination

6.1%

7.6%

4.3%

Table 1 shows the potential national-level net coverage error using PA for each of these categories in both the2010 and 2020 Censuses. PA shows an overcount in both 2020 and 2010. Two thousand and ten includes thetotal population and the non-Hispanic population. However, for 2020 we only show the non-Hispanic
population as the data needed to recode SOR are not yet available for the total population.

These findings should not be surprising given that the PES showed a net
overcount in 2020 and net coverage in 2010 that was not significantly different
from zero for the Asian American community. PA comes to basically the sameconclusions, but with a larger estimated overcount in 2010 than the PES. PA
provides additional insight into overcounts and undercounts at lower levels ofgeography.

PA by Lower Geographies
Potential overcounts in both 2010 and 2020 are not evenly distributed throughout
the country. For example, in 2020 undercounts of the Asian American population
in some of the interior Mountain West states are canceled out by estimatedovercounts in the West Coast, East Coast, and the South. The state-level PES in2020 showed net undercounts for the total population (all races) for several
southern states (including Florida, Tennessee, Mississippi, Arkansas, and Texas).
However, PA shows potential net overcounts for Asian Americans in all of thesestates in 2020, and in all but Mississippi and South Carolina in 2010.

38

Analysis: Asian American Community––2010 and 2020 Censuses
Map 6

Map 7

Percent Difference between 2010 Census Raceand Population Estimates, by StateAsian American, Alone Population

Percent Difference between 2020 Census Raceand Population Estimates, by StateAsian American, Alone Population

Error of Closure (%)

Error of Closure (%)-5% 0%-5% 0% 5% 10%

5% 10%

Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimates data for 2010 and 2020. Note: An error ofclosure value less than 0% indicates a potential undercount, i.e. the population estimate for API (Alone) was less than census results.

Maps 6 and 7 show the state-level measures of potential overcounts and undercounts for the 2010 and 2020 Censuses using PA.
We focus here on the Asian Alone or in Combination population as the more inclusive grouping. The trends are similar to the
Asian Alone population.

Geographic patterns emerge in the above maps, and PA also shows counties withpotential undercounts or overcounts. Though PA suggests a national overcount inboth 2010 and 2020, not all counties followed the same pattern. Indeed, in both
decennial censuses more than half of the counties were actually undercounted.
This indicates that the population in these potentially overcounted counties was(much) larger than the potentially undercounted ones.
Figure 18

2010 Census Accuracy:
Asian Alone or In Combination

2020 Census Accuracy:
Asian Alone or In Combination

32%

67%

1%

47%

52%

1%

of counties
overcountedof counties
undercountedof counties
no differenceof counties
overcountedof counties
undercountedof counties
no difference

Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimates data for 2010 and 2020.

Figure 18 shows that for the Asian population in both 2010 and 2020, while PA estimates an overcount at the national
level, more than half of the counties in both decades actually experienced undercounts.

39

Analysis: Asian American Community––2010 and 2020 Censuses

Mapping these counties and their potential undercounts and overcounts reveals
even more. In Map 8 we have a 2010 Census map of the potential undercounts andovercounts for the Asian American community. PA revealed that in 2010, morethan 5% of the population had been overcounted nationally, even though mostcounties still had undercounts. This is shown in the graphic. Studying where theovercounts happened matters because of the importance of a correct count forthe distribution of resources and political power; Southern California, the Bay Area
in California, the counties surrounding the Puget Sound in Washington, the
Houston area in Texas, the Chicago area in Illinois, and other highly populatedareas all saw potential overcounts in 2010.
Map 8

Percent Difference between 2010 Census Race andPopulation Estimates, by County
Asian American, Alone or in Combination Population
Error of Closure (%)
-125%

50%

0%

50%

125%

Map 8 shows the county-level
distribution of coverage error from
PA for the 2010 Census. In 2010,
there was a potential 5% overcountnationally using PA––even thoughmost counties still had
undercounts. However, Southern
California, the Bay Area in California,
the counties surrounding the Puget
Sound in Washington, the Houstonarea in Texas, the Chicago area in
Illinois, and other highly populatedareas all saw potential overcounts
in 2010.
Source: Authors' calculations using United States Census Bureau decennial census and postcensal population
estimates data for 2010. Note: An error of closure value less than 0% indicates a potential undercount, i.e. thepopulation estimate for Asian Americans (Alone or in Combination) was less than the census results.

40

Analysis: Asian American Community––2010 and 2020 Censuses

In 2020, many of these same places––including areas such as Southern California,
the Bay Area, the counties surrounding the Puget Sound in Washington, the
Houston area in Texas, the Chicago area in Illinois, and the New York City area––continued to have estimated net overcounts for Asian Americans. Similar to the2010 Census, estimated undercounts tended to cluster in more rural areas in the
Midwest, South, and Mountain West.
Map 9

Percent Difference between 2020 Census Race andPopulation Estimates, by County
Asian American, Alone or in Combination Population
Error of Closure (%)
-125%

50%

0%

50%

125%

Map 9 shows the coverage
estimates from PA for the Asian
American population in the 2020
Census. Many of the same areasthat saw overcounts in 2010
continued to experience them in
2020.
Source: Authors' calculations using United States Census Bureau decennial census and postcensal population
estimates data for 2010. Note: An error of closure value less than 0% indicates a potential undercount, i.e. thepopulation estimate for Asian Americans (Alone or in Combination) was less than the census results.

PA by age group
By using PA, we also reveal how overcounts and undercounts are distributed
unequally across the population. One of the key findings from DA is that young
children tend to be undercounted in the decennial census.⁹³ However, because of
the lack of data on the Asian American community in DA, we are not able todetermine if this finding applies to them.⁹⁴ Our analysis suggests that it does. Since
the population estimates data are available at the county level by race and by 5year age groups, we can report net coverage by age and by race using PA.

41

Analysis: Asian American Community––2010 and 2020 Censuses

For both the youngest children (ages 0–4) and the older population (ages 75 andabove), the Asian American Alone group appears undercounted in 2010. For the
Asian American Alone or in Combination group, there is a slight potential
overcount for the youngest children, while the older ages remain undercounted.
Older children and young adults have a much higher probability of an overcountfor both the Asian American Alone as well as the Alone or in Combination group.
The undercount of young children generally persisted for the total population in
2020, and it will be interesting to see if this holds for the Asian American Alone or
the Asian American Alone or in Combination community as well once the Bureau
makes the data available.⁹⁵
Figure 19

Coverage by Age Group, 2010
Asian American, Alone and Alone or in Combination Populations
Alone

Alone or in Combination

20

Error of Closure (%)

10
overcount0

undercount

-10

20 9
-2
4
25
-2
9
30
-3
4
35
-3
9
40
-4
4
45
-4
9
50
-5
4
55
-5
9
60
-6
4
65
-6
9
70
-7
4
75
-7
9
80
-8
4
85
+

-1

4
-1

15

9
5-

10

4
0-

-20

Age

Figure 19 shows the age
distribution of net coveragefor the Asian American (bothAlone as well as Alone or in
Combination) population in
the 2010 Census. The
youngest children were
undercounted for the Alone
population, but not the Aloneor in combination group. For
both groups the young adult
population was overcounted,
and those 75 and above were
undercounted.

Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimatesdata for 2010.

In 2010 throughout the country, a general overcount existed for the Asian
American Alone population ages 20–24. However, a few states (Montana,Oklahoma, Mississippi, and West Virginia) undercounted Asian Americans Alone in
the population ages 20–24. These states with smaller Asian American populations
might have had undercounts because they lack the community connections thatstates with larger Asian American populations have, and therefore may not benefit
from community-based census outreach.⁹⁶

42

Analysis: Asian American Community––2010 and 2020 Censuses
Map 10Percent Difference between 2010 Census Race andPopulation Estimates, by StateAsian American, Alone or in Combination Population: Ages 20–24
Error of Closure (%)
-10%

0%

25% 50%

Map 10 shows the coverage
estimates from PA for the Asian
American population ages 20–
24. Here we see that although
this group experiences highovercounts at the national level,some states still experience
undercounts (West Virginia,
Montana, and Mississippi).
Source: Authors' calculations using United States Census Bureau decennial census and postcensal population
estimates data for 2010. Note: An error of closure value less than 0% indicates a potential undercount, i.e. thepopulation estimate for Asian Americans (Alone or in Combination) was less than the census results.

At the county level in 2010, the Asian Alone or in Combination group had a slightovercount for the population between the ages of 0–4 and a larger overcount forthe population between the ages of 20–24. Although both of these groups had
potential overcounts in our analysis, the majority of counties were undercounted;
more urban, larger population counties generally were overcounted, while smaller,
more rural counties had undercounts.
Figure 20

2010 Census Accuracy:
Asian Alone or in Combination, Ages 0–4

2010 Census Accuracy:
Asian Alone or in Combination, Ages 20–24

27%

67%

6%

35%

57%

8%

of counties
overcountedof counties
undercountedof counties
no differenceof counties
overcountedof counties
undercountedof counties
no difference

Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimates data for 2010.

Figure 20 shows the percentage of counties that were undercounted or overcounted by select age groups for the 2010
Census. While the population ages 0–4 had a slight overcount at the national level, most counties were actually
undercounted. Likewise, the population ages 20–24 had a large overcount at the national level, but most counties wereactually undercounted for this group.

43

Local Snapshots: Asian American Case Studies
Case studies allow us to examine the quality of the decennial census
count for the Asian American community at a more detailed scale. Asian
Americans comprise just over 7% of the population of the United States,
but they are not distributed evenly geographically. We selected four
locations with sizable or distinctive Asian American communities. Our
first case study, Los Angeles County, California has one of the largest
populations of Asian Americans in the United States. Second, KingCounty, Washington has seen rapid growth of the Asian American
population. Our third case study, Houston, is distinctive not only for its
growth, but also given the large Vietnamese and Asian Indian
populations. Finally, we examine the five counties that make up New
York City (NYC). We chose NYC given the long history of Asian Americans
in the city and the significant Bangladeshi concentration here.

Common Findings: Census Coverage and Self-Response
Throughout the report we discuss how the recent net overcounts for the Asian American
population do not mean that the Asian American population was overcounted
everywhere. Our case studies reinforce this conclusion. In some of our local snapshots
we find undercounts for the Asian American community, while in others we findovercounts. These coverage errors changed over time––in fact, before 2000 the Asian
American population was undercounted. Put another way, in one decennial census anarea may have experienced a net overcount, while in another they experienced a net
undercount.
Below, we outline the key findings and avenues for future research across all of our casestudies before analyzing each case study individually.

Key Findings
At the national level, the census appears to have overcounted Asian Americans. Stateswith large Asian American populations––including California and New York––were
especially likely to have overcounts. But Asian Americans were not overcounted
everywhere. For example, our case studies show undercounts for both Los Angeles
County and King County, Washington (for the Asian Alone population only in 2020).

44

Future research should focus on improving our understanding of these coverage issues
by attending to which data source is causing the error, as well as why it is occurring.

Impact of Age on Census Accuracy
Comparing our case studies to the national average reveals where specific age groups
don’t follow national patterns.
The Asian American Alone population experienced a noticeable national undercount ofyoung children ages 0–4 (Alone or in Combination did not experience a national
undercount for these ages). All our case studies also show an undercount of young
children for the Asian Alone population, and most of the case studies show anundercount for young children for the Asian Alone or in Combination population. Thisshows how persistent the undercount of young children is.
We also find that for older children and young adults (ages 10–29), there is a larger
average overcount than in any other age group. This is the case in nearly all of our casestudies, and holds for both the Alone and the Alone or in Combination population.
Adults (ages 30–64) are closest to net zero coverage error on average. Again, thesefindings generally hold for our case studies, with some interesting exceptions. KingCounty in Washington and some of NYC’s counties show interesting patterns for someages within this span. Ages 65 and above have net undercounts on average. Some ofour case studies follow this pattern, while others do not.
More research is needed to better understand these age patterns. Are there differences
based on how much is spent on outreach? Is outreach more effective when it's done bymembers of the impacted group, and does the timing of the outreach matter? Perhaps
there are differences in the subgroups that make up each of these age cohorts in thesedifferent geographic locations. Additional research is needed to answer thesequestions.

Self-Response: Impact of Contextual Variables
Contextual variables––including citizenship status, housing tenure, and English
language ability––are thought to impact self-response rates. Self-response provides
valuable insight into where people are, and are not, responding to the census.
Researchers have hypothesized that self-response rates correlate with overall census
accuracy even though self-response rates are not technically a direct measure ofdecennial census accuracy.⁹⁷
Our case studies are limited in what we can say about the relationship between census
quality and self-response generally. Places like Los Angeles County, which had lower
response rates in areas with larger Asian American populations, experienced anundercount in 2020. King County, Washington also experienced an undercount in 2020
despite having higher self-response rates (both overall and in areas with larger Asian45

American populations) than the national average. Places like New York City, which had
many areas of low self-response rates, experienced overcounts in 2020. In sum, some ofour case studies suggest a relationship between self-response rates and census quality,
while others do not.
Regardless of any correlation, self-response is important because it is the highest
quality response type––thus, knowing self-response rates is a critical first step inplanning on how to best improve self-response in our communities. We find someevidence of the potential impact of citizenship status, housing tenure, and English
language ability on self-response, but none are supported all the time.
In general, census tracts with large, non-citizen Asian American populations tend to
experience lower self-response than other census tracts.
More research is needed to better understand how self-response impacts overall census
quality for the Asian American population, which also requires the Census Bureau tobegin providing measures of self-response by race. Additional research can help show
the extent to which self-response impacts overall census quality, and if it does so
differentially by race, ethnicity, age, or sex. Moreover, while housing tenure and English
language ability do seem to have some impact on self-response, more data are needed
to better define the relationship between these factors and self-response.⁹⁸ Experts
should study what other contextual variables impact self-response rates.
In order to undertake this research, more geographically granular measures of census
quality are sorely needed.⁹⁹ For instance, being able to examine how self-response
correlates with net coverage error at the county or city level can tell us a lot more than
the state or national levels. Lastly, researchers should study why factors such as housing
tenure, citizenship, and English language ability impact self-response and whether othervariables may also affect self-response rates in Asian American communities.

46

Los Angeles
County,California

Asian Americans make up approximately
15% of the 10 million people who live in the incredibly
diverse and geographically large Los Angeles
County¹⁰⁰––where the percentage in the county ismore than double the percentage of the Asian
American population in the United States overall.¹⁰¹
The graphics below provide data on Los Angeles
County, comparing Los Angeles’s Asian American
community to the rest of the nation.
Based on PA, Los Angeles County had apotential overcount in 2010, but then flipped to apotential undercount in 2020 for the
Asian Alone population.
dfdd

LOS ANGELES COUNTYASIAN POPULATION

Coverage

Asian Alone

Asian Alone or in Combination

Population Estimates

1,285,291

1,285,291

Census Population

1,207,397

1,311,755

Asian Alone

Asian Alone or in
Combination

Net Coverage (%)

6.3% undercount

2.0% overcount2000

12.3% (1,173,799)

13.2% (1,258,275)

Population Estimates

1,327,782

1,433,295

2010

14.2% (1,393,578)

15.6% (1,535,944)

Census Population

1,393,578

1,311,755

Net Coverage (%)

4.8% overcount

6.9% overcount2020

14.9% (1,489,041)

16.4% (1,639,661)

Population Estimates

1,495,320

1,630,180

Census Population

1,489,041

1,639,661

Net Coverage (%)

0.4% undercount

0.6% overcount

2000*

2010

2020

TOP 10 ASIAN SUBGROUPS
Alone or in Combination 2020
Los Angeles County

United States

Chinese

HOUSING TENURE AND ENGLISH LANGUAGE ABILITY
Los Angeles
CountyCalifornia

United States

Homeowners

54%

60%

60%

Renters

46%

40%

40%

Overall

35%

38%

35%

LEP** (ages 5+)

7%

7%

7%

Overall

65%

62%

65%

LEP** (ages 5+)

53%

47%

43%

Naturalized Citizen

66%

65%

59%

Non-Citizen

34%

35%

41%

Asian Alone 2020

Filipino
KoreanHousing Tenure

Japanese
Vietnamese

Native Born

Asian Indian
Cambodian
Other Asian
Taiwanese

Foreign Born

Thai
0%

10%

20%

30%

40%

Source: American Community Survey (ACS) 2020**LEP (Limited English Proficiency): those who speak English “less than very well"*For the data from 2000, the estimated population numbers come from the 1990 population estimates––before the census offered the option to selectmultiple races. For the first time in 2000, the census allowed respondents to select multiple races. Therefore, while we cannot differentiate between
Alone and Alone or in Combination for the Population Estimates, we can make that differentiation for the census population.

Source: ACS 2020

47

IMPACT OF AGE ON CENSUS ACCURACY
The undercount of young children is higher for the Asian American Alonegroup in Los Angeles County than in the United States as a whole. While national
data shows a slight overcount for young children in the Asian American Alone orin Combination group, there is an undercount of them in Los Angeles County.
The error of closure is very close to zero in Los Angeles County for ages 30–34, while asignificant Asian American overcount occurs for the same group for the United States as awhole.
The undercount of the older population in Los Angeles County is smaller than it is in the
country as a whole.
20

Asian Alone Coverage

dfdd

overcount

0-10Error of Closure (%)

Error of Closure (%)

Region10

Asian Alone or in
Combination Coverage

20

By Age Group: 2010

undercountLos Angeles County
United States-20

By Age Group: 201010

overcount0

undercountLos Angeles County-10

United States04
5
1 0 -9
-1
15 4
20 19
-2
25 4
-2
30 9
-3
35 4
-3
40 9
-4
45 4
-4
50 9
-5
55 4
-5
60 9
-6
65 4
-6
70 9
75 74
80 79
-8
4
85
+

04
5
1 0 -9
-1
15 4
-1
20 9
-2
25 4
-2
30 9
-3
35 4
-3
40 9
-4
45 4
-4
50 9
-5
55 4
-5
60 9
-6
65 4
-6
70 9
-7
75 4
-7
80 9
-8
4
85
+

-20

Age

Age

Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimates data for 2010.

Response Rate by Census Tract

SELF-RESPONSE RATESAreas with relatively large Asian American
populations in the 2020 Census had relatively
high response rates. This measure reveals
why the Asian American count should be dfdd
relatively good in Los Angeles County.
When a tract has an Asian American
population of 50% or more, we consistently
see self-response rates over 50%, with mostnear 75%.

25%

50%

75%

Source: United States Census Bureau, Tract Level Response Rates, 2020.
Note: Census tracts in white indicate no self response data reported.

Response Rate by County Tract

CITIZENSHIP

Non-Citizen Population

Percentage of Asian Alone
Population and Citizenship: 2020

Overall Self-Response Rate (%)

Some evidence
suggests thatcitizenship status
may have impacted
response rates.
Census tracts with alower level of
response among
the Asian American
population tended
to have more noncitizens.

Overall SelfResponse Rate

Asian Alone 2020

75

50

dfdd

Population with Citizenship
25%

25

50%

75%

Total Tract Population
0

0

25

50

5000

10000

75

Asian Alone Population (%)
Source: United States Census Bureau, Decennial Census, Tract Level Response Rates (2020), and ACS (2016-2020).

48

A relatively large Asian American community lives in KingCounty––the largest county in Washington State that
includes Seattle and several of its suburbs. According to the2020 Census, Asian Americans are 23.3% of the population of
the county––where more than 2,269,675 people (or 29.5% ofWashington State’s population) reside.

King County,
Washington

Because the Asian American community in King County issimilar to the national Asian American community on mostmeasures (including ethnicity, renter/owner proportions,and English language ability), we expect similar net
coverage patterns to national averages. While there aresome differences in the distribution of coverage by age, likethe national average, King County showed an estimatedovercount in both the 2010 and 2020 Censuses.
Based on PA, King County had a potential overcount in 2010,
dfdd
but then flipped to a potential undercount in 2020 for the
Asian Alone Population.

KING COUNTYASIAN POPULATION

2000

Coverage

Asian Alone

Asian Alone or in
Combination

11.0% (191,741)

12.6% (218,242)

2010

14.8% (286,648)

17.3% (333,338)

2020

20.0% (453,230)

23.3% (527,853)

2000*

2010

2020

TOP 10 ASIAN SUBGROUPS
Alone or in Combination 2020
King County

United States

Asian Alone

Asian Alone or in Combination

Population Estimates

189,340

189,340

Census Population

201,237

234,030

Net Coverage (%)

6.1% overcount

21.1% overcountPopulation Estimates

276,018

312,682

Census Population

286,648333,338

Net Coverage (%)

3.8% overcount

6.4% overcountPopulation Estimates

457,740

519,071

Census Population

453,230

527,853

Net Coverage (%)

1.0% undercount

1.7% overcount

HOUSING TENURE AND ENGLISH LANGUAGE ABILITY

ChineseAsian Alone 2020

King County

Washington

United States

Homeowners

58%

62%

60%

Renters

42%

38%

40%

Overall

33%

34%

35%

LEP** (ages 5+)

6%

6%

7%

Overall

67%

66%

65%

LEP** (ages 5+)

40%

41%

43%

Other Asian

Naturalized Citizen

50%

54%

59%

Laotian

Non-Citizen

50%

46%

41%

Asian Indian
Housing Tenure

Filipino
Vietnamese
Japanese

Native Born

KoreanCambodianTaiwanese

Foreign Born

0%

10%

20%

30%

**LEP (Limited English Proficiency): those who speak English “less than very well”

Source: ACS 2020

Source: ACS 2020*For the data from 2000, the estimated population numbers come from the 1990 population estimates––before the census offered the option to selectmultiple races. For the first time in 2000, the census allowed respondents to select multiple races. Therefore, while we cannot differentiate between
Alone and Alone or in Combination for the Population Estimates, we can make that differentiation for the census population.

49

IMPACT OF AGE ON CENSUS ACCURACYKing County and the United States as a whole have almost identicalundercount rates for the young (ages 0–4) Asian American Alone population.
There is a significant overcount that is much larger than the national average forthe young (ages 0–4) Asian Alone or in Combination population. One potential
reason for this could include people reporting race differently between birth certificates andthe decennial census.¹⁰² Specifically, they might only report one race (not Asian) on birthcertificates while reporting multiple races (including Asian) on the decennial census. These
children could be duplicates in the decennial census or miscounted for other reasons.
30

Asian Alone Coverage

dfdd

By Age Group: 201020
RegionError of Closure (%)

Error of Closure (%)

20
10overcount0-10Asian Alone or in
Combination Coverage

30

undercountKing County
United States

By Age Group: 201010
overcount0

undercount-10

King County
United States

-20
04
5
1 0 -9
-1
15 4
20 19
-2
25 4
-2
30 9
-3
35 4
-3
40 9
-4
45 4
-4
50 9
-5
55 4
-5
60 9
-6
65 4
-6
70 9
75 74
80 79
-8
4
85
+

04
5
1 0 -9
-1
15 4
-1
20 9
-2
25 4
-2
30 9
-3
35 4
-3
40 9
-4
45 4
-4
50 9
-5
55 4
-5
60 9
-6
65 4
-6
70 9
-7
75 4
-7
80 9
-8
4
85
+

-20

Age

Age

Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimates data for 2010.

SELF-RESPONSE RATES

Response Rate by Census Tract

Few tracts overall have response rates below 50%.
There is not a strong pattern between response rates
and the percentage of a tract population that is Asian
American. While some areas with a relatively large
dfdd
proportion of Asian Americans have very highresponse rates, others have low response rates
relative to the King County average of 77%. There are
relatively lower rates in the eastern part of KingCounty, in southern King County, and up into
downtown Seattle.

CITIZENSHIP

25%

50%

75%

Source: United States Census Bureau, Tract Level Response Rates, 2020.
Note: Census tracts in white indicate no self response data reported.

Response Rate by County Tract

Non-Citizen Population

Percentage of Asian Alone
Population and Citizenship: 2020

Overall Self-Response Rate (%)

Census tracts thathave a higher
proportion of Asian
Americans and havea higher percentage
of non-citizen Asian
Americans tend to
self-respond at lower
rates than places
with more Asian
Americans that are
citizens.

Overall Self-Response Rate

Asian Alone 2020

75

dfdd

50

Total Tract Population

25

0

0

5000

Population with Citizenship

10000

40
20
Asian Alone Population (%)

25%

50%

75%

60

Source: United States Census Bureau, Decennial Census, Tract Level Response Rates (2020), and ACS (2016-2020).

50

Harris County is noteworthy because the Asian
American population has increased significantly in the
county over the past 20 years. Made up of Houston,
Texas and several of its suburbs, it is home to over 4.7
million people––just over 16% of the population of thestate of Texas. Of these 4.7 million people, 7.4% are
Asian American. The subgroups that make up the Asian
American population differ from the national averages.
The largest group in the Asian American community in
Harris County is Vietnamese, followed by Asian Indianand then Chinese.¹⁰³

Harris County,
Texas

Based on PA, Harris County had a potential overcountdfdd 2020 Censuses.
in both the 2010 and

HARRIS COUNTYASIAN POPULATION

2000

Coverage

Asian Alone

Asian Alone or in
Combination

5.3% (181,356)

5.7% (194,941)

2010

6.4% (261,637)

7.0% (285,796)

2020

7.4% (348,281)

8.1% (382,276)

2000*

2010

2020

TOP 10 ASIAN SUBGROUPS

Asian Alone

Asian Alone or in Combination

Population Estimates

200,590

200,590

Census Population

184,808

200,666

Net Coverage (%)

8.2% undercount

0.04% overcountPopulation Estimates

241,117

264,882

Census Population

261,637

285,796

Net Coverage (%)

8.2% overcount

7.6% overcountPopulation Estimates

342,302371,482

Census Population

348,281382,276

Net Coverage (%)

1.7% overcount

2.9% overcount

Alone or in Combination 2020
Harris County

United States

HOUSING TENURE AND ENGLISH LANGUAGE ABILITY

Vietnamese

Asian Alone 2020

Harris County

Texas

United States

Homeowners

62%

64%

60%

Renters

38%

36%

40%

Overall

31%

32%

35%

LEP** (ages 5+)

8%

7%

7%

Overall

69%

68%

65%

LEP** (ages 5+)

47%

40%

43%

Cambodian

Naturalized Citizen

59%

55%

59%

Thai

Non-Citizen

41%

45%

41%

Asian Indian
Housing Tenure

Chinese
Filipino
Pakistani

Native Born

KoreanOther AsianJapanese

Foreign Born

0%

10%

20%

30%

**LEP (Limited English Proficiency): those who speak English “less than very well”

Source: ACS 2020

Source: ACS 2020*For the data from 2000, the estimated population numbers come from the 1990 population estimates––before the census offered the option to selectmultiple races. For the first time in 2000, the census allowed respondents to select multiple races. Therefore, while we cannot differentiate between
Alone and Alone or in Combination for the Population Estimates, we can make that differentiation for the census population.

51

IMPACT OF AGE ON CENSUS ACCURACY
The estimated coverage error by age for the Asian Alone population in Harris
County is similar to the national average.
The estimated overcount is higher for the young adult population (ages 15–29),
and the undercount for the oldest population (ages 85 and above) isn’t quite as large.
For the Asian Alone or in Combination population, the pattern compared to the national
average is similar for all groups except the youngest (ages 0–4). In Harris County there is an
estimated undercount, compared to a slight overcount at the national level. This means thatin the 2010 Census, fewer children were identified as Asian (Alone or in Combination) than inbirth records.
Asian Alone Coverage

dfdd

Asian Alone or in
Combination Coverage

30

By Age Group: 2010
Error of Closure (%)

Regionovercount0

undercountHarris County
United States

Error of Closure (%)

20

20

-20

By Age Group: 201010
overcount0

undercountHarris County
United States

-10

04
5
1 0 -9
-1
15 4
-1
20 9
-2
25 4
-2
30 9
-3
35 4
-3
40 9
-4
45 4
-4
50 9
-5
55 4
60 59
-6
65 4
-6
70 9
75 74
-7
80 9
-8
4
85
+

04
5
1 0 -9
-1
15 4
-1
20 9
-2
25 4
-2
30 9
-3
35 4
-3
40 9
-4
45 4
-4
50 9
-5
55 4
-5
60 9
-6
65 4
-6
70 9
-7
75 4
-7
80 9
-8
4
85
+

-20

Age

Age

Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimates data for 2010.

Response Rate by Census Tract

SELF-RESPONSE RATESOverall SelfResponse Rate

Areas with higher proportions of Asian
Americans generally have higher response rates,with a few exceptions.
Low and high response rates are not distributed
dfdd
evenly across the county.
Many of the lower response rates are clusteredaround downtown, in part of northern Harris
County, and sprinkled in a few areas in both theeast and west.

50%

75%

Source: United States Census Bureau, Tract Level Response Rates, 2020.
Note: Census tracts in white indicate no self response data reported.

Response Rate by County Tract

CITIZENSHIP

Non-Citizen Population

Percentage of Asian Alone
Population and Citizenship: 2020

Overall Self-Response Rate (%)

There is a clear
correlation
betweencitizenship andresponse rates
for Asian
Americans inHarris County.

25%

Asian Alone 2020

80

60

dfdd
Population with Citizenship

40

50%

25%

75%

Total Tract Population
0

20
0

40
20
Asian Alone Population (%)

5000

10000

60

Source: United States Census Bureau, Decennial Census, Tract Level Response Rates (2020), and ACS (2016-2020).

52

The Asian American population has grown significantly in New York
City throughout the last 20 years, and now makes up over 16% of the
population of the largest and one of the most diverse cities in the
United States.¹⁰⁴ In 2020, the city had over 8 million people spreadthroughout its five counties (boroughs): New York (Manhattan), Kings(Brooklyn), Bronx (Bronx), Richmond (Staten Island), and Queens(Queens). Chinese and Asian Indian are the largest Asian ethnicities in
the city. And while Bangladeshis are not in the top ten Asian subgroups
in the United States, they are the fifth largest group in New York City––
signaling that this is one of the largest Bangladeshi concentrations in
the U.S. The graphics below provide information on the boroughs andcounties, the city as a whole, the state, and the nation.

New York City,
New York

Since New York City is made up of five counties, there are morecitizenship and English language ability data to analyze. Overall,
Brooklyn, Manhattan, and Queens have the largest Asian American
populations. Brooklyn and Manhattan have slightly more non-citizens
than the national average, but Queens has a much larger share of the
Asian American non-citizen population. Further, in all of the boroughs
other than Manhattan, both the foreign-born and native-born
populations are less likely to speak English very well (LEP) than thenational average.

NEW YORK CITYASIAN POPULATION
Asian Alone

Asian Alone or in
Combination

2000

10.5% (838,074)

11.1% (891,718)

2010

13.3% (1,086,296)

14.3% (1,167,545)

16.1% (1,420,318)

17.4% (1,531,522)

2020

Based on PA, New York City had a potential overcount in both the2010 and 2020 Censuses. This was the case in most of the fivecounties (or boroughs) that make up New York City as well (see thetable below for exceptions).
Coverage: New York City2000*

TOP 10 ASIAN SUBGROUPS
Alone or in Combination 2020
New York City

United States

2010

ChineseAsian Indian
KoreanFilipino

2020

Bangladeshi
Other Asian

Asian Alone

Asian Alone or in Combination

Population Estimates763,272

763,272

Census Population

849,755

913,923

Net Coverage (%)

10.7% overcount

18.0% overcountPopulation Estimates

1,042,963

1,109,827

Census Population

1,086,296

1,167,545

Net Coverage (%)

4.1% overcount

5.1% overcountPopulation Estimates

1,241,591

1,322,328

Census Population

1,420,318

1,531,522

Net Coverage (%)

13.4% overcount

14.7% overcountdfdd

Pakistani

Asian Alone 2020

Japanese
Vietnamese
Taiwanese

New York City

United States

Overall30%

35%

LEP (ages 5+)

12%

7%

Overall70%

65%

LEP (ages 5+)

59%

43%

Naturalized Citizen

57%

59%

Non-Citizen

43%

41%

Native Born
0%

10%

20%

30%

40%

50%

Source: ACS 2020

Asian Alone 2020

Housing
TenureNew York City

United States

Homeowners

43%

60%

Renters

57%

40%

Foreign Born

Source: ACS 2020*For the data from 2000, the estimated population numbers come from the 1990 population estimates––before the census offered the option to selectmultiple races. For the first time in 2000, the census allowed respondents to select multiple races. Therefore, while we cannot differentiate between
Alone and Alone or in Combination for the Population Estimates, we can make that differentiation for the census population.

Source: ACS 202053

County-Level Demographics
PA Analysis, Housing Tenure and English Language Ability

Bronx

Population: 1,472,654

Coverage: Bronx County

Asian Alone or in
Combination

Asian Alone

Population Estimates

50,91750,917

Census Population

49,54656,367

Net Coverage (%)

2.7% undercount

10.2% overcountPopulation Estimates

57,172

62,161

Census Population

57,06264,994

Net Coverage (%)

0.2% undercount

4.5% overcountPopulation Estimates

58,226

61,887

Census Population

72,15469,090Net Coverage (%)

21.4% overcount

24.4% overcount

2000*

2010

2020

Housing
TenureHomeowners Renters
39%

NativeBorn

Foreign
Born

Overall

LEP (ages 5+)

32%

13%

OverallLEP(ages 5+)Naturalized
Citizen

NonCitizen

68%

55%

57%

43%

Source: ACS 2020

Brooklyn
Asian Alone 2020

Housing
TenureNativeBorn

Foreign
Born

Homeowners Renters
43%

Overall33%

15%Overall

LEP

Naturalized
Citizen

NonCitizen

67%

69%

59%

41%

2000*

2010

2020
Source: ACS 2020

Population: 2,736,074
Asian Alone

Asian Alone or in
Combination

Population Estimates

160,917160,917

Census Population

199,289

215,724

Net Coverage (%)

21.3% overcount

29.1% overcountPopulation Estimates

259,819

278,559

Census Population

269,251

289,764

Net Coverage (%)

3.6% overcount

3.9% overcountPopulation Estimates

322,772

348,255

Census Population

378,683

419,208

Net Coverage (%)

16.0% overcount

18.5% overcountCoverage: Kings County

57%

LEP
(ages 5+)

Asian Alone 2020

61%

dfdd

54

Manhattan
Housing
TenureNative
Born

24%

76%

Overall

LEP (ages 5+)

34%

6%

Foreign
Born

OverallNaturalized
Citizen

NonCitizen

66%

46%

50%

50%

2010

2020

169,637

169,637

Census Population

151,033

162,381Net Coverage (%)

11.6% undercount

4.4% undercountPopulation Estimates

184,390

195,662

Census Population

184,345

203,376

Net Coverage (%)

0.02% undercount

3.9% overcountPopulation Estimates

204,630

222,556

Census Population

222,265

250,582

Net Coverage (%)

8.3% overcount

11.9% overcount2020

Population: 2,405,464

Coverage: Queens County

2000*

Population Estimates

2010

Source: ACS 2020

QueensAsian Alone or in
Combination

2000*

Asian Alone 2020
LEP
(ages 5+)

Asian Alone

Coverage: New York County

Homeowners Renters

Asian Alone or in
Combination

Asian Alone

Population Estimates

352,004

352,004

Census Population

423,599

450,922Net Coverage (%)

18.6% overcount

24.6% overcountPopulation Estimates

504,976

533,589

Census Population

539,400

570,085

Net Coverage (%)

6.6% overcount

6.6% overcountPopulation Estimates

601,159

631,117

Census Population

687,936

719,333

Net Coverage (%)

13.5% overcout

13.1% overcount

Housing
TenureNativeBorn

Foreign
Born

Homeowners

Renters

51%

49%

Asian Alone 2020

Housing
TenureHomeowners Renters
80%

LEP (ages 5+)

28%

14%

OverallLEP(ages 5+)Naturalized
Citizen

NonCitizen

72%

60%

57%

43%

Source: ACS 2020

Coverage:
Richmond County

20%
2000*

Overall

LEP (ages 5+)

32%

12%

OverallLEP(ages 5+)Naturalized
Citizen

NonCitizen

68%

dfdd
54%

67%

33%

NativeBorn

Source: ACS 2020

Asian Alone 2020

OverallStaten Island

Foreign
Born

Population: 1,694,251

2010

2020

Population: 495,747
Asian Alone

Asian Alone or in
Combination

Population Estimates

29,797

29,797

Census Population

26,28828,529Net Coverage (%)

12.5% undercount

4.4% undercountPopulation Estimates

36,60639,856

Census Population

36,23839,326Net Coverage (%)

1.0% undercount

1.3% undercountPopulation Estimates

54,80458,513

Census Population

59,28063,309Net Coverage (%)

7.9% overcount

7.9% overcount55

IMPACT OF AGE ON CENSUS ACCURACY
All counties show an estimated undercount of young (ages 0–4) Asian
American children (both for the Alone as well as the Alone or in
Combination population). In the United States nationally, there is not anundercount among the Asian American Alone or in Combination population
ages 0–4.
For Richmond County (Staten Island), there is a much larger undercount for young children(ages 0–9) than for the rest of the boroughs or the United States as a whole. New YorkCounty (Manhattan) has a much larger estimated overcount for the young adult population
than the other boroughs or the United States nationally.

Asian Alone Coverage by Age Group: 201025

Error of Closure (%)

RegionBronx (Bronx County)
overcountundercount0

Brooklyn (Kings County)
New York City
Manhattan (New York County)-25

Queens (Queens County)
Staten Island (Richmond County)
United States

-50

9

4

9

4

9

4

9

4

9

-3

-4

-4

-5

-5

-6

-6

-7

-7

-8

40

45

50

55

60

65

70

75

80

4

4
35

+

9

-3
30

85

4

-2
25

9

-2

-1

20

4
-1

15

9
5-

10

4
0-

dfdd

Age
Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimates data for 2010.

Asian Alone or in Combination Coverage by Age Group: 2010

25

Bronx (Bronx County)
overcountundercount0

Brooklyn (Kings County)
New York City
Manhattan (New York County)-25

Queens (Queens County)
Staten Island (Richmond County)
United States

9

4

9

4

9

4

9

4

9

-3

-4

-4

-5

-5

-6

-6

-7

-7

-8

40

45

50

55

60

65

70

75

80

4

4
35

+

9

-3
30

85

4

-2
25

9

-2

-1

20

4
-1

15

9
5-

dfdd

10

4

-50

0-

Error of Closure (%)

RegionAge
Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimates data for 2010.

56

SELF-RESPONSE RATESThe overall variabilityof response rates by tract isfairly high in New York City
overall. Tracts with more Asian
Americans also vary in their response rates.

Response Rate by Census Tract: 2020
Overall Self-Response Rate
25%

50%

Bronx (Bronx County)

75%

Manhattan(New York County)
Response
Rate by Tract

The relationship between the proportionof Asian Americans in a tract and selfresponse seems to be non-linear. That is,Staten Islandin tracts where 50–60% of the population (Richmond County)
Queensis Asian American, the response rate
(Queens County)
Brooklyntends to be very high. There are declines(Kings County)
in self-response in tracts where morethan 70% of the population is Asian
Source: United States Census Bureau, Tract Level Response Rates, 2020.
Note: Census tracts in white indicate no self response data reported.
American.
Clusters of low self-response rates exist in each of the boroughs, and tend to cluster morein Queens, the Bronx, and Brooklyn.
Non-Citizen Asian Alone Population

CITIZENSHIP

By Census Tract: 2020

Bronx

Overall Self-Response Rate (%)(Bronx County)
The Asian American non-citizen
Population with Citizenshipcommunity is not evenly distributed
throughout New York City.
50% 75%25%
dfdd
Some of the areas with relatively high
Manhattan(New York County)
non-citizen populations are also areas
with relatively low self-response rates.
There is evidence of citizenship impacting
Staten Island(Richmond County)
Asian American response rates. We canQueenssee that in tracts with lower response(Queens County)
Brooklynrates with Asian Americans that the(Kings County)
population tends
Response Rate by Census Tract
to be a higher
Percentageof
Asian
Alone Population and Citizenship State: 2020
proportion of80
non-citizens.

60

40

Population with Citizenship
25%

20

dfdd

50%

75%

Total Tract Population
0

0

20

50

5000

10000

75

Asian Alone Population (%)
Source: United States Census Bureau, Decennial Census, Tract Level Response Rates (2020), and ACS (2016-2020).

57

Analysis: NHPI Community––2010 and 2020 Censuses

Analysis: The Quality of the Census for the NHPI Community in the2010 and 2020 Censuses
According to the 2020 Census, the NHPI community comprises approximately
0.48% of the total United States population. The NHPI community does not have astatistically significant undercount or overcount according to the PES. But this lackof a statistically significant finding may be due to the small sample size of the
population, which leads to large margins of error.
National Net Coverage Error by Race Category: PA Method

Table 2

2010

2020

Non-Hispanic Population

Total Population

Non-Hispanic Population

NHPI Alone

8.3%

13.4%

4.7%

NHPI Alone or in
Combination

8.7%

13.7%

6.9%

Table 2 shows the estimated net coverage for NHPI Alone and NHPI Alone or in Combination for the 2010 and2020 Censuses using PA. For 2010, we separated out the total NHPI population from the non-Hispanic NHPI
community so that we can make comparisons to 2020. Due to current data limitations, we are only able to provide
data for the non-Hispanic NHPI community in 2020, for which we could create a modified race file.

PA estimates an overcount for the NHPI population in the 2010 and 2020 decennial
censuses. In 2010, this estimated overcount was larger for the total NHPI (i.e., both
Hispanic and non-Hispanic) population than for the non-Hispanic NHPI population.
The remainder of the analysis examines the total population for 2010 and the nonHispanic population for 2020 (where possible) to provide comparisons between
these groups.¹⁰⁵ Given the size of the NHPI population that reports more than one
race, we will focus on the NHPI Alone or in Combination group instead of NHPI
Alone.

PA by Lower Geographies
For most states in 2010, PA estimates an overcount existed for the NHPI
community. A few states, mostly in the Upper Midwest and Northeast, do not follow
this pattern. These are areas that show an undercount for the NHPI population but
where the total population generally sees overcounts or counts that are notstatistically different from zero: North Dakota, South Dakota, Minnesota, Wisconsin,
Michigan, Illinois, Maine, Vermont, New Hampshire, Massachusetts, West Virginia,
and Mississippi.

58

Analysis: NHPI Community––2010 and 2020 Censuses

In 2020 the patterns were not terribly different. Potential undercounts are only
present in a handful of states, mostly in the Upper Midwest, the Northeast, and theSouth: Michigan, Massachusetts, Connecticut, Rhode Island, Delaware, Maryland,
Georgia, and Florida (the actual states with potential undercounts did change insome cases from 2010).
Map 12

Map 11

Percent Difference between 2010 Census Raceand Population Estimates, by StateNHPI, Alone or in Combination Population

Percent Difference between 2020 Census Raceand Population Estimates, by StateNHPI, Alone or in Combination Population

Error of Closure (%)

-25%

0%

Error of Closure (%)

25%

-25%

0%

25%

Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimates data for 2010 and 2020. Note: An error ofclosure value less than 0% indicates a potential undercount, i.e. the population estimate for NHPI Americans (Alone or in Combination) was less than the census results.

Map 11 shows the state-level variation of the decennial census count quality for NHPIs in the 2010 Census. Map 12 shows thestate-level variation of the decennial census count quality for NHPIs in the 2020 Census.

At the state level, the places with the largest NHPI communities have estimatedovercounts, just as we see nationally. The pattern is more complicated for counties.
Examining the percentage of counties that had estimated overcounts andundercounts in both 2010 and 2020 reveals that over 50% of counties in 2010 andnearly 40% of counties in 2020 were undercounted for the NHPI Alone or in
Combination group. In comparison, nationally there were estimated overcounts forboth decades for NHPIs, and for the vast majority of states there were overcounts.
Since there is a national overcount, it is likely that most of these undercounts occur
in places with smaller NHPI communities. Differences in coverage are not spread
evenly across the country. County-level data corroborates this conclusion. Becauseof the pronounced differences in census coverage for NHPIs between counties,
national numbers are likely hiding variation.

59

Analysis: NHPI Community––2010 and 2020 Censuses
Figure 212010 Census Accuracy:
NHPI Alone or In Combination

2020 Census Accuracy:
NHPI Alone or In Combination

44%

51%

5%

59%

37%

4%

of counties
overcountedof counties
undercountedof counties
no differenceof counties
overcountedof counties
undercountedof counties
no difference

Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimates data for 2010 and 2020.

Figure 21 shows the percentage of counties that were undercounted or overcounted in both 2010 and 2020 for the
NHPI community. Here we see that even though PA estimates an overcount in both decades, a significant numberof counties experience undercounts.

Mapping the estimated undercounts and overcounts illustrates where, and begins
to hint at why, this variation occurs.
Map 13

Percent Difference between 2010 Census Race andPopulation Estimates, by County
NHPI, Alone or in Combination Population
Error of Closure (%)
-125%

50%

0%

50%

125%

The 2010 Census NHPI Alone or in
Combination estimated coverage
error shows overcounts andundercounts spread relatively
randomly in most of the country.
But some patterns emerge: There isa higher percentage ofundercounts through the Midwest
and central Texas. On the other
hand, most of the West Coastshows estimated overcounts for the
NHPI Alone or in Combination
population.
Source: Authors' calculations using United States Census Bureau decennial census and postcensal population
estimates data for 2010. Note: An error of closure value less than 0% indicates a potential undercount, i.e. thepopulation estimate for NHPI Americans (Alone or in Combination) was less than the census results.

60

Analysis: NHPI Community––2010 and 2020 Censuses

How does this compare to the 2020 Census estimated coverage of the NHPI Aloneor in Combination population? Some of the coverage patterns look similar, whileothers are completely different. For example, the West Coast is still generally abastion of estimated overcounts. However, a few key counties—such as Los
Angeles County in California—turned from estimated overcounts in 2010 toestimated undercounts in 2020. The Midwestern undercounts in 2010 are now
mostly overcounts. Since the NHPI population in most counties is very small, this
could be partially explained by very small population changes, but the pattern isstill quite stark.
Map 14

Percent Difference between 2020 Census Race andPopulation Estimates, by County
NHPI, Alone or in Combination Population
Error of Closure (%)
-125%

50%

0%

50%

125%

The 2020 Census NHPI Alone or in
Combination estimated coverage
error shows overcounts andundercounts spread relatively
randomly in most of the country.
Some of the patterns we saw in2010 are different in 2020. The
Midwest is estimated to have more
overcounts while the Southeast andNortheast continue their trends of
having more counties with
undercounts.
Source: Authors' calculations using United States Census Bureau decennial census and postcensal population
estimates data for 2010. Note: An error of closure value less than 0% indicates a potential undercount, i.e. thepopulation estimate for NHPI Americans (Alone or in Combination) was less than the census results.

PA by age group
Similar to the overall population, differences in coverage by age exist within the
NHPI community.¹⁰⁶ The overall overcount of the NHPI population in 2010 holds formost age groups, except for ages 65 and above.

61

Analysis: NHPI Community––2010 and 2020 Censuses

Unlike the overall population, a potential net undercount of the youngest children
does not appear to exist for NHPIs. There is an overcount of these ages, though it is
not as large as the overcount for older children and young adults. This is an
unexpected finding. Perhaps the NHPI community is more likely to report their
young children in the decennial census, or birth records for the NHPI community
may be more likely to misreport race.¹⁰⁷ The highest overcounts by age are thoseof older children and younger adults. This aligns with the pattern seen in theoverall population.
Figure 22

Coverage by Age Group, 2010
NHPI, Alone and Alone or in Combination Populations
Alone

Alone or in Combination

Error of Closure (%)

20

overcount0

undercount

-20

04
59
10
-1
4
15
-1
9
20
-2
25 4
-2
9
30
-3
4
35
-3
9
40
-4
4
45
-4
9
50
-5
4
55
-5
60 9
-6
4
65
-6
9
70
-7
4
75
-7
9
80
-8
4
85
+

-40

Age
Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimatesdata for 2010.

Age distributions of overcounts and undercounts for NHPIs vary geographically.
Examining the youngest children, young adults, and the older populations by state
reveals that the youngest children and young adults are not overcounted
everywhere, and the population ages 65 and above is not undercounted
everywhere. While the youngest children (those ages 0–4) are overcounted at thenational level and in most states with large NHPI populations, they are often
undercounted in other states.¹⁰⁸
Similar patterns emerge for ages 20–24 and ages 0–4. The majority of states withlarge NHPI populations show an overcount, while multiple states with smaller
NHPI populations show undercounts.

62

Analysis: NHPI Community––2010 and 2020 Censuses
Map 15Percent Difference between 2010 Census Race andPopulation Estimates, by StateNHPI, Alone or in Combination Population: Ages 0–4
Error of Closure (%)-50% -25%

0% 25% 50%

Map 15 shows the state-level
coverage for PA for ages 0–4 for
the NHPI community in 2010.
Though the NHPI community
does not experience a net
undercount at the national level
for this age group, many statesdo.
Source: Authors' calculations using United States Census Bureau decennial census and postcensal population
estimates data for 2010. Note: An error of closure value less than 0% indicates a potential undercount, i.e. thepopulation estimate for NHPIs (Alone or in Combination) was less than the census results.

Map 16Percent Difference between 2010 Census Race andPopulation Estimates, by StateNHPI, Alone or in Combination Population: Ages 20–24
Error of Closure (%)-50% -25% 0%

25% 50%

Map 16 shows the state-level
coverage for PA for ages 20–24
for the NHPI community in 2010.
Though the NHPI community
did not experience a net
undercount at the national level
for this age group, many states
did (though fewer than did forages 0–4).
Source: Authors' calculations using United States Census Bureau decennial census and postcensal population
estimates data for 2010. Note: An error of closure value less than 0% indicates a potential undercount, i.e. thepopulation estimate for NHPIs (Alone or in Combination) was less than the census results.

63

Analysis: NHPI Community––2010 and 2020 Censuses

Finally, while NHPIs ages 65 and above are undercounted nationally, they are
overcounted in Hawaii—where the NHPI population is the largest (Nevada also has
an overcount).
Map 17

Percent Difference between 2010 Census Race andPopulation Estimates, by StateNHPI, Alone or in Combination Population: Ages 65 and Above
Error of Closure (%)-50% -25%

0% 15%

Map 17 shows the state-level
coverage for PA for ages 65 andabove for the NHPI community in2010. This age group experiences
high undercounts at the national
level. With the exception of
Hawaii and Nevada, all otherstates had undercounts.
Source: Authors' calculations using United States Census Bureau decennial census and postcensal population
estimates data for 2010. Note: An error of closure value less than 0% indicates a potential undercount, i.e. thepopulation estimate for NHPIs (Alone or in Combination) was less than the census results.

Estimated undercounts and overcounts for different age groups also vary at thecounty level. Some counties had no difference between the estimates and thedecennial census due to the small NHPI populations (or even complete lack of
population). Even though ages 0–4 and ages 20–24 tend to be overcounted, they
are not overcounted in all counties. There were more counties with undercounts
than overcounts for both ages 0–4 and ages 20–24. Therefore, overcounts for those
age groups occur in places with larger NHPI populations.

64Analysis: NHPI Community––2010 and 2020 Censuses

The NHPI community ages 65 and above is undercounted nationally, but there area few counties that are overcounted for this group. Further research is needed on
the inclusion of NHPI elders (ages 65 and above) in the decennial census, theirpotential overestimation in the population estimates, or both.
Figure 232010 Census Accuracy:
NHPI Alone or in Combination, Ages 0–4

2010 Census Accuracy:
NHPI Alone or in Combination, Ages 20–24

35%

41%

24%

35%

37%

28%

of counties
overcountedof counties
undercountedof counties
no differenceof counties
overcountedof counties
undercountedof counties
no difference2010 Census Accuracy:
NHPI Alone or in Combination, Ages 65 and Above

16%

58%

26%

of counties
overcountedof counties
undercountedof counties
no difference

Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimates data for 2010.

Figure 23 shows the proportion of counties that experience undercounts and overcounts for three different age groups according
to PA. Even though ages 0–4 and ages 20–24 are both overcounted at the national level, a significant number of counties still
have undercounts. The 65 and above population is undercounted significantly at the national level, and only 16% of counties
experienced an overcount of this group.

65

Local Snapshots: Native Hawaiian andPacific Islander Case Studies
Case studies allow us to examine the quality of the decennial census
count for the NHPI community at a more detailed scale. NHPIs compriseabout 0.48% of the population of the United States, but they are notdistributed evenly geographically. We selected four locations with sizable
or distinctive NHPI communities and because of the diversity of their
NHPI origins.¹⁰⁹ Our first case study, Hawaii, has the largest population of
NHPIs in the United States, and we analyze census coverage for both thestate and its five counties. Second, Los Angeles County, California has
another large NHPI community. Our third case study, the NHPI
community in Northwestern Arkansas, is almost entirely Marshallese andconstitutes the largest Marshallese population in the United States.
Finally, within the state of Utah, there are large Tongan and Samoan
populations in Utah and Salt Lake Counties.
Below, we outline the key findings and avenues for future research across all of our casestudies before analyzing each case study individually.

Key Findings
At the national and the state level, the census appears to have overcounted NHPIs.
States with large NHPI populations––including Hawaii and California––were especiallylikely to have overcounts. But NHPIs were not overcounted everywhere. For example,
our case studies show undercounts for both Los Angeles County and Hawaii County (forthe NHPI Alone or in Combination population only).
Moreover, these coverage errors change over time. Before 2000, the NHPI population
was undercounted. Put another way, in one decennial census an area may haveexperienced a net overcount. In another decennial census, the same area experienced a
net undercount. Future research should focus on improving our understanding of thesecoverage issues by attending to which data source is causing the error, as well as why it
is occurring.

Impact of Age on Census Accuracy
Comparing our case studies to the national average reveals where specific age groups
don’t follow national patterns.

66

There was not a national undercount of young children among NHPIs in 2020. This is
surprising since other racial groups had national undercounts of young children. Ourcase studies showed overcounts of young children. Some case studies had larger
overcounts, while others were closer to net zero coverage error.
Our analysis shows that older NHPI children and young adults (ages 10–29) have thelargest average overcount at the national level for any age group. Our case studies also
show an overcount for NPHIs between the ages of 10–29. Unlike the national average, in
our case studies, NHPI children and young adults are not always the largest overcount.
Nationally, NHPI adults (ages 30–64) are closest to net zero coverage error on average,
and the existing overcount slowly converges towards zero with increases in age. In ourcase studies, we also see adults ages 30–64 closest to net zero coverage error, withsome interesting exceptions: Many adults ages 25–64 are undercounted in Los Angeles
County. Finally, undercounts were common for NHPIs ages 65 and above nationally andin our case studies aside from Los Angeles County, where a large overcount occurred.
More research is needed to better understand these age patterns. Are there differences
based on how much is spent on outreach? Is outreach more effective when it's done bymembers of the impacted group, and does the timing of the outreach matter? Perhaps
there are differences in the subgroups that make up each of these age cohorts in thesedifferent geographic locations. Additional research is needed to answer these questions.

Self-Response: Impact of Contextual Variables
Contextual variables––including citizenship status, housing tenure, and English
language ability––are thought to impact self-response rates. Self-response provides
valuable insight into where people are, and are not, responding to the census.
Researchers have hypothesized that self-response rates correlate with overall census
accuracy even though self-response rates are not technically a direct measure ofdecennial census accuracy.¹¹⁰
Our case studies are limited in what we can say about the relationship between census
accuracy and self-response generally. Los Angeles County experienced an undercountin 2020. But tracts there do not have many NHPIs, and thus we cannot make arguments
about the impact of NHPI response rates on census quality. On the other hand, we wereable to make more definitive claims about Hawaii because tracts there had a higher
concentration of NHPIs; here, we saw similar overcounts and response rates (comparedto the national average overcount and average national response rate).
Regardless of any correlation, self-response is important because it is the highest
quality response type––thus, knowing self-response rates is a critical first step inplanning on how to best improve self-response in our communities. We find someevidence of the potential impact of citizenship status, housing tenure, and English
language ability on self-response, but none are supported all the time.

67

In general, census tracts with large NHPI populations that have more non-citizens
experience lower self-response. The relationship between citizenship and self-response
is especially nuanced for NHPIs. According to the 2014 report, A Community of Contrasts:
Native Hawaiians and Pacific Islanders in the United States, “While Native Hawaiians
and many Pacific Islanders born in Hawai‘i, Guam, or the Commonwealth of theNorthern Mariana Islands are U.S. citizens, some Pacific Islanders are foreign-born and,depending on their country of birth, may hold different types of immigration
statuses.”¹¹¹ Therefore, our analysis of the impact of citizenship on self-response will only
apply to certain NHPI groups, while not being applicable to others.
More research is needed to better understand how self-response impacts overall census
quality for the NHPI population, which also requires the Census Bureau to begin
providing measures of self-response by race. Additional research can help show theextent to which self-response impacts overall census quality, and if it does so
differentially by race, ethnicity, age, or sex. Moreover, while housing tenure and English
language ability do seem to have some impact on self-response, more data are needed
to better define the relationship between these factors and self-response.¹¹² Experts
should study what other contextual variables impact self-response rates.
In order to undertake this research, more geographically granular measures of census
quality are sorely needed.¹¹³ For instance, being able to examine how self-response
correlates with net coverage error at the county or city level can tell us a lot more than
the state or national levels. Lastly, researchers should study why factors such as housing
tenure, citizenship, and English language ability impact self-response and whether othervariables may also affect self-response rates in NHPI communities.

68Hawaii is home to the largest NHPI community in the
United States.¹¹⁴ The NHPI community has kept almost
exact pace with the population growth of Hawaii from2000 to 2020. The NHPI Alone or in Combination
population made up nearly a quarter of the populationof Hawaii in 2000 and continued to do so in 2020. Mostof this population is Native Hawaiian, as approximately
half of all Native Hawaiians who live in the UnitedStates live in Hawaii. The majority of the NHPI
community who is foreign-born lives in Honolulu
County and comprises the largest group of NHPIs who
do not speak English very well (commonly referred toas Limited English Proficient or LEP).

HawaiiHAWAII NHPI POPULATIONNHPI Alone

NHPI Alone or in
Combination

2000

9.6% (115,964)

23.4% (283,430)

2010

10.2% (138,292)

26.4% (358,951)

2020

10.3% (149,949)

23.7% (345,220)

Based on PA, Hawaii had a potential overcount in 2010
and 2020 for both the NHPI Alone as well as the NHPI
Alone or in Combination populations. This held true inalmost every county in Hawaii. The only exception is
the NHPI Alone or in Combination population in the2020 Census for Hawaii County.
Coverage: Hawaii

2000*

TOP 5 NHPI SUBGROUPS
Alone or in Combination 20202010
HawaiiNHPI Alone

NHPI Alone or in Combination

Population Estimates

768,660

768,660

Census Population

626,808

988,415

Net Coverage (%)

20.3% undercount25.0% overcountPopulation Estimates

120,469

289,888

Census Population

138,292

358,951

Net Coverage (%)

13.8% overcount

21.3% overcountPopulation Estimates

136,357

329,764

Census Population

149,949

345,220

Net Coverage (%)

9.5% overcount

4.6% overcountUnited States

Native Hawaiian
Samoan2020

Other Micronesiandfdd

TonganNHPI Alone 2020

Marshallese
0%

20%

40%

60%

80%

Housing
TenureHawaii

United States

Homeowners

45%

42%

Renters

55%

58%

United States

Overall

83%76%

LEP (ages 5+)5%5%Overall

17%24%

LEP (ages 5+)

50%

36%

Naturalized Citizen

21%

41%

Non-Citizen

79%

59%

Native Born

Source: American Community Survey (ACS) 2020NHPI Alone 2020

Hawaii

Foreign Born

Source: ACS 2020
Source: ACS 2020*For the data from 2000, the estimated population numbers come from the 1990 population estimates––before the census offered the option to selectmultiple races. For the first time in 2000, the census allowed respondents to select multiple races. Therefore, while we cannot differentiate between
Alone and Alone or in Combination for the Population Estimates, we can make that differentiation for the census population.

69

County-Level Demographics
PA Analysis, Housing Tenure and English Language Ability

Hawaii County

Population: 200,629

Coverage: Hawaii County

NHPI Alone or in
Combination

NHPI Alone

Population Estimates

85,521

85,521

Census Population

57,506

117,331

Net Coverage (%)

39.1% undercount

31.5% overcountPopulation Estimates

20,80753,163

Census Population

22,93063,146Net Coverage (%)

9.7% overcount

17.2% overcountPopulation Estimates

24,19559,892

Census Population

26,36259,152Net Coverage (%)

8.6% overcount

1.2% undercount

2000*

2010

2020

Housing
TenureNativeBorn

Foreign
Born

Homeowners Renters
42%

NHPI Alone 2020

58%

Overall

LEP (ages 5+)

87%

6%

OverallLEP(ages 5+)Naturalized
Citizen

NonCitizen

13%

48%

10%

90%

Source: ACS 2020

Hawaii County was one of the largest counties with an estimated undercount in the 2020
Census and was the only county with more NHPI homeowners than renters. This is surprisingbecause homeownership is generally positively correlated with census response rates;¹¹⁵
therefore, we would not have expected undercounts.

Honolulu County
NHPI Alone 2020

Housing
TenureHomeowners Renters
40%

2000*
NativeBorn

Foreign
Born

Overall

LEP (ages 5+)

81%

5%

OverallLEP(ages 5+)Naturalized
Citizen

NonCitizen

19%

53%

23%

77%

2010

2020

dfdd
Source: ACS 2020NHPI Alone

NHPI Alone or in
Combination

Population Estimates

571,953

571,953

Census Population

487,688

730,492

Net Coverage (%)

15.9% undercount

24.3% overcountPopulation Estimates

78,051

185,158

Census Population

92,676

235,520

Net Coverage (%)

17.1% overcount

23.9% overcountPopulation Estimates

89,607

212,713

Census Population

97,596228,009

Net Coverage (%)

8.5% overcount

6.9% overcountCoverage: Honolulu County

60%

Population: 1,016,508

70

County-Level Demographics (continued)
PA Analysis, Housing Tenure and English Language Ability

Kauai County

Population: 73,298

Coverage: Kauai County

2000*

2010

2020

NHPI Alone or in
Combination

NHPI Alone

Population Estimates

36,86436,864

Census Population

26,81946,029Net Coverage (%)

31.6% undercount

22.1% overcountPopulation Estimates

6,035

15,320Census Population

6,178

17,497

Net Coverage (%)

2.3% overcount

13.3% overcountPopulation Estimates

6,109

16,213

Census Population

6,884

17,328Net Coverage (%)

11.9% overcount

6.7% overcount

Housing
TenureNativeBorn

Foreign
Born

Homeowners

Renters

56%

44%

Overall

LEP (ages 5+)

90%

2%

OverallLEP(ages 5+)Naturalized
Citizen

NonCitizen

10%

54%

20%

80%

Source: ACS 2020

Maui County
NHPI Alone 2020

Housing
TenureHomeowners Renters
47%

53%

Overall

LEP (ages 5+)

86%

1%

OverallLEP(ages 5+)Naturalized
Citizen

NonCitizen

Foreign
Born

14%

31%

18%

82%
Source: ACS 2020

2010

2020

Population: 164,745
NHPI Alone

NHPI Alone or in
Combination

Population Estimates

74,30274,302

Census Population

54,69894,461Net Coverage (%)

30.4% undercount

23.9% overcountPopulation Estimates

15,51736,188

Census Population

16,46442,734

Net Coverage (%)

5.9% overcount

16.6% overcountPopulation Estimates

16,40440,902

Census Population

19,092

40,713

Net Coverage (%)

15.2% overcount

0.5% undercountCoverage: Maui County

2000*
NativeBorn

NHPI Alone 2020

dfdd

Note: In our analysis we do not include Kalawao County because of the small population size.

71

IMPACT OF AGE ON CENSUS ACCURACY
In general, the data follow the national average with some exceptions:Children ages 5–9 are undercounted in all Hawaiian counties except
Honolulu.
The overcount for young adults is lower in Hawaii (approximately 20%) than it
is nationally (approximately 30%).
Hawaii County seems to have smaller undercounts for the population ages 65 and above(from approximately a 10% overcount to approximately a 30% undercount), while Kauai
tends to have larger undercounts for the population ages 65 and above (from anapproximately 20% to 60% undercount) versus the state and national averages.
For the NHPI Alone or in Combination population, coverage by age group is even closer tothe national average.
The overcounts for the youngest (ages 0–9) as well as for those in their 30s are slightly
higher than the national average (younger children were overcounted between 15–25%
and those in their 30s were overcounted 20–25%). However, the shape of the age
distribution is generally the same.
NHPI Alone Coverage by Age Group: 201020

Error of Closure (%)

Region0

overcount

HawaiiundercountHawaii County
Honolulu County

dfdd

-20

Kauai County
Maui County
United States

-40

4

9

4

9

4

9

4

9

4

9

-3

-4

-4

-5

-5

-6

-6

-7

-7

35

40

45

50

55

60

65

70

75

-8
4
85
+

9

-3
30

80

4

-2
25

9

-2

20

4

-1
15

9

-1

5-

10

4
0-

-60

Age
Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimates data for 2010.

NHPI Alone or in Combination Coverage by Age Group: 201025

overcountundercount0

Hawaii
Hawaii County
Honolulu County
Kauai County
Maui County-25

United States

9

4

9

4

9

4

9

4

9

-3

-4

-4

-5

-5

-6

-6

-7

-7

-8

35

40

45

50

55

60

65

70

75

80

4

4
-3

30

+

9
-2

25

85

4

9
-1

15

-2

4
-1

9

10

4

5-

20

dfdd

-50
0-

Error of Closure (%)

RegionAge
Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimates data for 2010.

72

SELF-RESPONSE RATESThere is not a strong
correlation between the
NHPI population and selfresponse rates by census tract
throughout Hawaii.
As the percentage of the NHPI population
increases in a census tract, the response
rate for that tract generally gets closer tothe average response rate for the state.
That is, the highest and lowest selfresponse rates seem to occur in places
with relatively few NHPI residents.

Response Rate by Census Tract: 2020

Honolulu County

Kauai County

Kalwao County

Response Rate by Tract
Overall Self-Response Rate
25%

50%

Maui County

75%

Hawaii County
Source: United States Census Bureau, Tract Level Response Rates, 2020.
Note: Census tracts in white indicate no self response data reported.

While we would expect lower rates in both rural and urban areas generally, some of themore rural areas show lower self-response rates, while more urban areas show higher
self-response rates.¹¹⁶

CITIZENSHIP

Non-Citizen NHPI Alone Population
By Census Tract: 2020

Most NHPIs who are not citizens are
Honolulu County
Kauai County
located in Honolulu County.
Kalwao County
There are relatively few non-citizen
dfdd
NHPIs in the rest of the state.
For census tracts with larger non-citizen
Maui County
NHPI populations, especially in
Population with Citizenship
Honolulu County, some have lower than
average self-response rates. Others do
25%
50% 75%have average to above average selfHawaii County
response rates.
Response Rate by Census Tract
Percentage of NHPI Alone Population and Citizenship State: 2020

Overall Self-Response Rate (%)

75

50
Population with Citizenship
25%

25

75%

Total Tract Population
0

0

50%

20

40

5000

10000

60

NHPI Alone Population (%)
Source: United States Census Bureau, Decennial Census, Tract Level Response Rates (2020), and ACS (2016-2020).

73

Arkansas has a relatively large Marshallese population located in thenorthwestern part of the state. While the NHPI community is generally
quite small in Arkansas, Marshallese make up a larger share of the NHPI
population in those parts of the state.¹¹⁷ This small NHPI population is
growing. In 2000, NHPIs were less than 0.15% of the total state
population. By 2020, this increased by almost 8,700 people to make up
nearly 0.6% of the population (for NHPI Alone or in Combination)––an
approximately 300% increase.

Arkansas

ARKANSASNHPI POPULATIONNHPI Alone

NHPI Alone or in
Combination

2000

0.7% (1,930)

0.12% (3,223)

2010

0.23% (6,685)

0.29% (8,594)

2020

0.48% (14,461)

0.57% (17,252)

A number of factors—including citizenship, English proficiency, andhomeownership—may impact the overall census quality for the NHPI
population in Arkansas. First, the NHPI community in Arkansas isapproximately two-thirds foreign born, because most NHPIs in the state
are foreign-born Marshallese. Nearly 90% of this foreign-born population
are non-citizens, and over half are Limited English Proficiency (LEP)—
potentially impacting decennial census response. Homeowners are
more likely to respond to the census than renters, and only 6% of NHPIs
in Arkansas own their homes (compared with 42% of NHPIs nationally).
Thus, we suspect NHPIs in Arkansas would have lower response rates
based on their low rates of homeownership.¹¹⁸ Overall, the NHPI
population is still quite small in Arkansas, meaning it is hard to draw
statistically significant conclusions based on analysis, but importantquestions are still raised for further research.

Based on PA, Arkansas had a potential overcount in the 2000, 2010,
and 2020 Censuses for the NHPI population (both Alone and Aloneor in Combination).
Coverage: Arkansas

2000*

TOP 5 NHPI SUBGROUPS
Alone or in Combination 2020
Arkansas

2010

United States

Marshallese
Other Pacific Islander

2020

Native Hawaiian
SamoanNHPI Alone

NHPI Alone or in Combination

Population Estimates

19,87819,878

Census Population

22,764

28,774

Net Coverage (%)

13.5% overcount

36.6% overcountPopulation Estimates

3,793

5,940

Census Population

6,685

8,594

Net Coverage (%)

55.2% overcount

36.5% overcountPopulation Estimates

11,532

13,755

Census Population

14,461

17,252Net Coverage (%)

22.5% overcount

22.6% overcount

dfdd

NHPI Alone 2020

Other Micronesian
0%

25%

50%

75%

Arkansas

United States

Overall37%76%

LEP (ages 5+)

11%

5%

Overall63%

24%

LEP (ages 5+)

61%

36%

Naturalized Citizen

11%

41%

Non-Citizen

89%

59%

Native Born

Source: ACS 2020NHPI Alone 2020

Housing
TenureArkansas

United States

Homeowners

6%

42%

Renters

94%

58%

Foreign Born

Source: ACS 2020*For the data from 2000, the estimated population numbers come from the 1990 population estimates––before the census offered the option to selectmultiple races. For the first time in 2000, the census allowed respondents to select multiple races. Therefore, while we cannot differentiate between
Alone and Alone or in Combination for the Population Estimates, we can make that differentiation for the census population.

Source: ACS 2020

74

IMPACT OF AGE ON CENSUS ACCURACY
In general, until about age 44, the NHPI population has a higher
estimated overcount in Arkansas than NHPIs nationally.
From ages 45–64, the NHPI population in Arkansas generally follows thenational pattern.
For those ages 65 and above, estimated undercounts in Arkansas are significantly higher
than they are nationally. This data point is concerning for older (ages 65 and above) NHPIs in
Arkansas because they are missed at much higher rates.¹¹⁹

NHPI Alone Coverage by Age Group: 2010
200

RegionArkansas
overcount0

undercountBenton County
United States

-100

4

9

4

9

4

9

4

9

4

9

4

9

-2

-3

-3

-4

-4

-5

-5

-6

-6

-7

-7

25

30

35

40

45

50

55

60

65

70

75

9

-2

-1

20

4
-1

15

9
5-

10

4
0-

-8
4
85
+

dfdd

-200

80

Error of Closure (%)

100

Age
Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimates data for 2010.

NHPI Alone or in Combination Coverage by Age Group: 2010
200

100

Error of Closure (%)

RegionArkansas
overcountundercount0

Benton County
United States

-100

4

9

4

9

4

9

4

9

4

9

-3

-4

-4

-5

-5

-6

-6

-7

-7

35

40

45

50

55

60

65

70

75

-8
4
85
+

9

-3
30

80

4

-2
25

9

-2

-1

20

4
-1

15

9
5-

10

4
0-

-200

Age
Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimates data for 2010.

75

SELF-RESPONSE RATESWhile the NHPI population in Arkansas is small, over 20% of some
census tracts are NHPI—a relatively large self-clustering.
Tracts with more than 20% NHPIs have higher self-response rates (usually
between 60-70%) than the national average.¹²⁰
Benton County is a microcosm of this pattern. It has one census tract with an over 10%
concentration of the NHPI population, which has over a 70% self-response rate.
There are high response rates surrounding Little Rock and its suburbs, and in northwestern
Arkansas where the Marshallese population is located.

dfdd
Response Rate by Census Tract:
2020
Arkansas

Benton County

Overall Self-Response Rate
25%

50%

75%

Source: United States Census Bureau, Tract Level Response Rates, 2020.
Note: Census tracts in white indicate no self response data reported.

A Look at Benton County, Arkansas...
NHPI Alone

NHPI Alone or in
Combination

Population Estimates

989

989

Census Population

1,907

2,280

Net Coverage (%)

63.4% overcount

79.0% overcountPopulation Estimates

544

685

Census Population

775

1,004

Net Coverage (%)

35.0% overcount

37.7% overcountPopulation Estimates

1,994

2,287

Census Population

2,601

3,075

Net Coverage (%)

26.4% overcount

29.4% overcountPA Coverage: Benton County

2000*

2010

2020

TOP 5 NHPI SUBGROUPS
Alone or in Combination 2020
Benton County

United States

Marshallese
Other Pacific Islander
Native Hawaiian
SamoanChamorro
0%

25%

50%

75%

100%

Source: ACS 2020

HOUSING TENURE AND ENGLISH LANGUAGE ABILITY

Housing
TenureHomeowners Renters
2%98%

NativeBorn

OverallLEP(ages 5+)

27%

18%

Foreign
Born

Overall

LEP

Naturalized
Citizen

NonCitizen

73%

61%

5%

95%

76

CITIZENSHIP STATUS
Because of the limited data available, the impact of citizenship on selfresponse for the NHPI population in Arkansas is inconclusive.
There is a grouping in northwest Arkansas of foreign-born, largely noncitizen NHPIs.
The Marshallese population is covered by the Compact of Free Association
Migrants, which complicates the relationship between citizenship status and self
response.¹²¹
Examining census tracts with more than 10% NHPIs reveals that the lowest self-response
rates tended to be in places that had a higher proportion of non-citizens. When we look at
Benton County we see some areas with large proportions of non-citizens in the NHPI
population. This is due to the aforementioned Marshallese population being the major NHPI
group in this area, which drives the lower non-citizen rates.
Arkansas

Non-Citizen NHPI
Alone Population
By Census Tract: 2020
Population with Citizenship
25%

50%

75%

Benton County

dfdd
Source: United States Census Bureau, Decennial Census, Tract Level Response Rates (2020), and ACS (2016-2020).

80

Benton County
Overall Self-Response Rate (%)

Response Rate by Census Tract
Percentage of NHPI Alone Population andCitizenship State: 2020

Overall Self-Response Rate (%)

90

dfdd

Arkansas

70

70

60

50

6
NHPI Alone Population (%)

3

9

50

Population with Citizenship
50%

25%

30

75%

Total Tract Population
0

5

10

15

5000

10000

20

NHPI Alone Population (%)
Source: United States Census Bureau, Decennial Census, Tract Level Response Rates (2020), and ACS (2016-2020).

77

Outside of Hawaii, Los Angeles County has the largest
population of NHPIs in the United States with 3.5% of the total
NHPI population. This diverse and geographically large county
with a population of over 10 million people¹²² has consistently
had a population of about 0.6% NHPI Alone or in Combination
over the last 20 years.¹²³ Other Pacific Islanders are the largest
group of NHPIs in Los Angeles County, followed closely by
Native Hawaiians and Samoans. Therefore, the NHPI populationin Los Angeles County is slightly less likely to be foreign-born(approximately 25%) than the nation as a whole (approximately30%). Further, the NHPI community in Los Angeles County isslightly less likely to own their home than the national averagefor the NHPI community. While we might expect a better
census response rate given the number of NHPI citizens in
Los Angeles County, this is mitigated by the high percentage of
NHPI renters (because renters are less likely than homeowners
to respond to the Census).

Los Angeles
County,California

Based on PA, Los Angeles County had a potential overcountin 2010, but then flipped to a potential undercount in 2020
for the NHPI Alone Population.

LOS ANGELES COUNTYNHPI POPULATIONNHPI Alone

NHPI Alone or in
Combination

2000

0.4% (33,598)

0.6% (53,480)

2010

0.4% (36,443)

0.6% (62,945)

2020

0.2% (21,327)

0.4% (44,206)

Coverage: Los Angeles

2000*

2010

TOP 5 NHPI SUBGROUPS
2020

Alone or in Combination 2020
Los Angeles

NHPI Alone

NHPI Alone or in Combination

Population Estimates

1,285,291

1,285,291

Census Population

1,207,397

1,311,755

Net Coverage (%)

6.3% undercount

2.0% overcountPopulation Estimates

35,83760,508

Census Population

36,44362,945Net Coverage (%)

1.7% overcount

4.0% overcountPopulation Estimates

21,92344,220Census Population

21,327

44,206Net Coverage (%)

2.8% undercount

0.03% undercountdfdd

United States

Other Pacific Islander

NHPI Alone 2020

Native Hawaiian

Housing Tenure

Los Angeles

California

United States

Homeowners

35%

45%

42%

Renters

65%

55%

58%

Overall

76%

71%

76%

LEP** (ages 5+)

4%

5%

5%

Overall

24%

29%

24%

LEP** (ages 5+)

29%

29%

36%

Naturalized Citizen

55%

60%

41%

Non-Citizen

24%

40%

59%

SamoanNative Born

Chamorro
Tongan0%
Source: ACS 2020

10%

20%

30%

40%

50%
Foreign Born**LEP (Limited English Proficiency): those who speak English “less than very well”*For the data from 2000, the estimated population numbers come from the 1990 population estimates––before the census offered the option to selectmultiple races. For the first time in 2000, the census allowed respondents to select multiple races. Therefore, while we cannot differentiate between
Alone and Alone or in Combination for the Population Estimates, we can make that differentiation for the census population.

Source: ACS 2020

78

IMPACT OF AGE ON CENSUS ACCURACY
Compared with the United States as a whole, the overcounts for NHPIs in LA
County are smaller. In fact, some age groups actually turn into undercounts.
Generally, up until about age 65, the patterns between age groups are
similar to the national pattern (e.g., ages 15–24 have a higher overcount thanages 25–64).
For ages 65 and above, the pattern for NHPIs in Los Angeles completely diverges from thenational average. Instead of a substantial national undercount, there is a substantial
estimated overcount in Los Angeles County.
NHPI Alone Coverage

Region10

overcountundercount0-10

By Age Group: 201020
Error of Closure (%)

Error of Closure (%)

20

NHPI Alone or in
Combination Coverage

dfdd

By Age Group: 2010

Los Angeles County
United States-20

10
overcount0

undercountLos Angeles County-10

United States04
5
1 0 -9
-1
15 4
20 19
-2
25 4
-2
30 9
-3
35 4
-3
40 9
-4
45 4
-4
50 9
-5
55 4
-5
60 9
-6
65 4
-6
70 9
75 74
80 79
-8
4
85
+

04
5
1 0 -9
-1
15 4
-1
20 9
-2
25 4
-2
30 9
-3
35 4
-3
40 9
-4
45 4
-4
50 9
-5
55 4
-5
60 9
-6
65 4
-6
70 9
-7
75 4
-7
80 9
-8
4
85
+

-20

Age

Age

Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimates data for 2010.

Response Rate by Census Tract

SELF-RESPONSE RATESOverall SelfResponse Rate

The largest tract level concentration of the NHPI
community is under 4%.
We cannot draw any conclusions about therelationship between the presence of the NHPI
dfdd
community and tract-level self-response rates
because there are no tracts with very large NHPI
communities.
NHPIs in Los Angeles County seem to be relatively
spread out throughout the county.

50%

75%

Source: United States Census Bureau, Tract Level Response Rates, 2020.
Note: Census tracts in white indicate no self response data reported.

Response Rate by County Tract

CITIZENSHIP

Non-Citizen Population

Percentage of NHPI Alone
Population and Citizenship: 2020NHPI Alone 202075
Overall Self-Response Rate (%)

We cannot draw
any strong
conclusions about
the impact of
citizenship for the
NHPI community
on tract-level selfresponse rates
because there areno tracts with morethan 4% NHPIs.

25%

50

dfdd

Population with Citizenship
25%

25

50%

75%

Total Tract Population
0

0

1

2
NHPI Alone Population (%)

5000

10000

3

Source: United States Census Bureau, Decennial Census, Tract Level Response Rates (2020), and ACS (2016-2020).

79

Salt Lake andUtah Counties, Utah

More than 3.27 million people live in Utah, and 1.7% of the state’s
population are NHPIs.¹²⁴ Salt Lake and Utah Counties are the two largest
counties in Utah, making up just over 56% of the state’s population. The
state’s largest cities, the state’s largest universities, and the state’s
largest NHPI communities are in Salt Lake and Utah Counties.
The NHPI population has grown in Salt Lake and Utah Counties fromjust over 18,000 in 2000 to just below 42,000 in 2020. Most of thispopulation is Samoan, Tongan, or Native Hawaiian. NHPIs’ citizenshiprates in these counties are similar to the national average for NHPIs. The
NHPI community in Utah and Salt Lake Counties have higher English
language abilities than the country as a whole. Finally, there are no
discernable differences in home ownership between the NHPI
community in Utah and Salt Lake Counties and the national average.
Given these similarities, we would expect to see similar or slightly higher
self-response rates than the national average, since English language
ability is believed to be positively correlated with census response rates.

Based on PA, Salt Lake and Utah Counties had a potential overcountin both the 2010 and 2020 Censuses of NHPI Alone as well as Alone orin Combination.

SALT LAKE COUNTYNHPI POPULATION

Coverage: Salt Lake County

2000*

NHPI Alone

NHPI Alone or in
Combination

2000

1.3% (11,699)

1.6% (14,404)

2010

1.6% (16,565)

2.1% (21,451)

2020

1.8% (21,314)

2.4% (28,610)

2010

2020

UTAH COUNTYNHPI POPULATIONNHPI Alone

NHPI Alone or in
Combination

2000

0.6% (2,203)

1.0% (3,630)

2010

0.8% (4,089)

1.5% (7,596)

2020

1.0% (6,652)

2.0% (13,138)

Source: ACS 2020NHPI Alone

NHPI Alone or in Combination

Population Estimates

33,31033,310Census Population

35,59243,006Net Coverage (%)

6.6% overcount

25.4% overcountPopulation Estimates

14,136

18,337

Census Population

16,564

21,451Net Coverage (%)

15.8% overcount

15.7% overcountPopulation Estimates

20,324

26,310Census Population

21,314dfdd

28,610Net Coverage (%)

4.8% overcount

8.4% overcount

NHPI Alone

NHPI Alone or in Combination

Population Estimates

7,343

7,343

Census Population

6,290

9,490

Net Coverage (%)

15.5% undercount

25.5% overcountPopulation Estimates

3,519

6,520

Census Population

4,089

7,596

Net Coverage (%)

15.0% overcount

15.3% overcountPopulation Estimates

6,053

11,214

Census Population

6,652

13,138Net Coverage (%)

9.4% overcount

15.8% overcountCoverage: Utah County

2000*

2010

2020

*For the data from 2000, the estimated population numbers come from the 1990 population estimates––before the census offered the option to selectmultiple races. For the first time in 2000, the census allowed respondents to select multiple races. Therefore, while we cannot differentiate between
Alone and Alone or in Combination for the Population Estimates, we can make that differentiation for the census population.

80

County-Level Demographics

Salt Lake County
TOP 5 NHPI SUBGROUPS

Housing
TenureAlone or in Combination 2020
Salt Lake County

Homeowners Renters
45%

NHPI Alone 2020

55%

United States

TonganNativeBorn

SamoanNative Hawaiian

Foreign
Born

Other Pacific Islander

Overall

LEP** (ages 5+)

69%

2%

Overall

LEP** (ages 5+)

Naturalized Citizen

Non-Citizen

31%

23%

55%

45%

Other Micronesian
0%

10%

20%

30%

40%

Source: ACS 2020

50%

Source: ACS 2020

Utah County
Housing
TenureNHPI Alone 2020

Homeowners Renters
39%

61%

TOP 5 NHPI SUBGROUPS
Alone or in Combination 2020
Utah County

NativeBorn

Foreign
Born

Overall

LEP** (ages 5+)

72%

2%

Overall

LEP** (ages 5+)

Naturalized Citizen

Non-Citizen

28%

24%

60%

40%

United States

SamoanNative Hawaiian
TonganOther Pacific Islander
Other Polynesian

Source: ACS 2020

0%

10%

20%

30%

40%

50%

Source: ACS 2020

dfdd**LEP (Limited English Proficiency): those who speak English “less than very well”

81IMPACT OF AGE ON CENSUS ACCURACY
In general, NHPIs in the state of Utah, Utah County, and Salt Lake County
all show the same patterns for age distribution of undercounts andovercounts as the national average.
For the NHPI Alone population ages 70 and above, there is a large estimatedovercount in Utah County in 2010.¹²⁵

NHPI Alone Coverage by Age Group: 2010

Error of Closure (%)

50

25

RegionSalt Lake County
United States
overcount0

Utah

undercountUtah County

4

9

4

9

4

9

4

9

4

9

4

9

-2

-3

-3

-4

-4

-5

-5

-6

-6

-7

-7

25

30

35

40

45

50

55

60

65

70

75

9

-2

-1

20

4
-1

15

9
5-

10

4
0-

-25

dfdd

Age

Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimates data for 2010.

NHPI Alone or in Combination Coverageby Age Group: 201020

RegionSalt Lake County
overcount0

undercountUnited States
Utah
Utah County

4

9

4

9

4

9

4

9

4

9

4

9

-2

-3

-3

-4

-4

-5

-5

-6

-6

-7

-7

25

30

35

40

45

50

55

60

65

70

75

9

-2

20

4

-1
15

9

-1

5-

10

4

-20

0-

Error of Closure (%)

40

Age
Source: Authors' calculations using United States Census Bureau decennial census and postcensal population estimates data for 2010.

82

SELF-RESPONSE RATESIn Salt Lake County, there are lower response rates in places where more
NHPIs live. This is noteworthy because we know that overall the NHPI
community is estimated to be overcounted in Salt Lake County, meaning therole of Nonresponse Followup had to be substantial here.
Relatively lower response rates flow up
Response Rate by Census Tract: 2020
the I-15 corridor. The suburbs havehigher response rates as well as in Salt
Salt Lake County
Lake City moving up towards the
Manhattan(New York County)
University of Utah.¹²⁶
The areas with higher response rates
tend to be in wealthier areas of the city,while lower response rates are in lesswealthy areas.
Overall Self-Response Rate
25%

50%

75%

There is not a large enough concentrationof NHPIs to use these self-response data
for Utah County.

Source: United States Census Bureau, Tract Level Response Rates, 2020.
Note: Census tracts in white indicate no self response data reported.

CITIZENSHIP VARIABLE IMPACT

Non-Citizen NHPI Alone Population
By Census Tract: 2020

Areas with relatively lower response rates
also have a larger percentage of nondfdd
citizen NHPIs.
Places with larger NHPI populations inSalt Lake City tend to have lower
response rates. However, more researchwould be needed to determine if this isdirectly impacted by NHPIs’ citizenshipstatus given the small sample size oftracts with high proportions of NHPIs.

Salt Lake County

Population with Citizenship
25%

50%

75%

Response Rate by Census Tract
Percentage of NHPI Alone Population and Citizenship State: 2020

Overall Self-Response Rate (%)

80

Salt Lake County
Population with Citizenship

60

25%

50%

75%

Total Tract Population

40
0

0

5

10

5000

10000

15

NHPI Alone Population (%)
Source: United States Census Bureau, Decennial Census, Tract Level Response Rates (2020), and ACS (2016-2020).

83Analysis: The Quality of Census Counts for
Asian American and NHPI Communities

NOTES
⁸⁹ While it would be an interesting addition to this report if we could look at census quality for the Pacific Island territoriesof the United States, this is not possible because annual population estimates are not created for these geographic areas.
⁹⁰ Some of the differences in these maps are inflated due to changes in racial categories between the population
estimates (only single race reporting) and the 2000 Census (multiple race reporting).
⁹¹ This difference is likely due to different patterns in reporting Alone versus Alone or in Combination in each region. In
places where more people reported Alone or in Combination––especially where they may not have chosen AANHPI as
their race if only given one choice––we would expect larger differences in undercounts between these two measures.
⁹² The data needed to recode SOR are not yet available for the total population, so we were only able to look at the nonHispanic Asian and NHPI communities for 2020. As further data from the 2020 Census become available, we will update
this analysis to include the total population.
⁹³ See O’Hare, William P. Differential Undercounts in the U.S. Census Who Is Missed? 1st ed. 2019. Cham: Springer Nature,
2019, accessed at: https://link.springer.com/chapter/10.1007/978-3-030-10973-8_5.
⁹⁴ As we saw in the DA section above, the Black and Hispanic communities have higher undercounts for ages 0–4 thanthe population overall. It is unclear if the Asian American community would follow the trend of the Black and Hispanic
population, the population as a whole, or some pattern of their own.
⁹⁵ Unfortunately, the data do not yet exist to make this comparison for 2020, though it will be available some time in 2023
when the demographic and housing characteristics file is released.
⁹⁶ For examples of community outreach that was funded, see: https://funderscommittee.org/wpcontent/uploads/2018/03/FCI-Key2020CensusFunderMilestones-Jan2019.pdf;
https://funderscommittee.org/resource/2020-census-funder-toolkit/; andhttps://funderscommittee.org/resource/census-2020-state-landscape-scan/.
⁹⁷ The reason self-response can’t officially serve as a measure of decennial census accuracy is because the Census Bureau
uses techniques such as Nonresponse Followup and imputation to improve the count, especially in areas with low selfresponse rates.
⁹⁸ Providing avenues for language support up front will help optimize self-response and remove potential cases from theNonresponse Followup workload. For more information, see letter from Asian American, Native Hawaiian and PacificIslander community-based organizations raising language support concerns is available at https://www.advancingjusticeaajc.org/sites/default/files/201810/AANHPI%20Census%20Language%20Support%20Program%20Letter%20October%202018%20Final.pdf. See also senate
letter raising concerns about language support program: https://twitter.com/hansilowang/status/1075886626864357376.
⁹⁹ For instance, being able to examine how self-response correlates with net coverage error at the county or city level cantell us a lot more than the state or national levels.
¹⁰⁰ According to the 2020 Census.
¹⁰¹ See, for example: Abelmann, Nancy, and John Lie. Blue Dreams: Korean Americans and the Los Angeles Riots.
Cambridge, Mass: Harvard University Press, 1995 and Kurashige, Scott. The Shifting Grounds of Race: Black and JapaneseAmericans in the Making of Multiethnic Los Angeles. Princeton, N.J: Princeton University Press, 2008.
¹⁰² Getting the race of a child from a birth certificate is not a straightforward task. One must use the race of the parents totry and estimate what the reported race of the child will be in a future census when reported by their parents or bythemselves. For further information on this process, see the Kidlink Method section of
https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020da_methodology.pdf,
pgs. 4-6.

84

Analysis: The Quality of Census Counts for Asian
American and NHPI Communities (continued)

NOTES¹⁰³ According to historian Roy Vu, Vietnamese refugees originally settled in Houston because the economy was strong,
there was a low cost of living, the climate was similar to Vietnam, and they were close to the ocean and able to fish. See
Rosie Nguyen, “ABC13 shares stories of Vietnamese refugees who made all-or-nothing escape and rebuilt their lives,” June
1, 2022, ABC13.com, accessed at https://abc13.com/vietnamese-vietnam-war-refugees-immigrants/11913968/.
For more information on Asian Americans in Houston, see Quraishi, Uzma. Redefining the Immigrant South : Indian andPakistani Immigration to Houston During the Cold War. Chapel Hill: The University of North Carolina Press, 2021 and Vu,
Roy, 'Natives of a Ghost Country: The Vietnamese in Houston and Their Construction of a Postwar Community', in Jigna
Desai, and Khyati Y. Joshi (eds), Asian Americans in Dixie: Race and Migration in the South Urbana: University of Illinois
Press, 2013.
¹⁰⁴ For a history of New York City’s Chinatown, see Tchen, John Kuo Wei. New York before Chinatown: Orientalism and theShaping of American Culture, 1776-1882. Baltimore: Johns Hopkins University Press, 1999. For more recent demographic
changes, see https://www.aafederation.org/aaf-our-work/research-2/;
https://www.aafederation.org/aaf-our-work/census-center/geographic-profiles/;
https://www.aafederation.org/research/state-of-change-asian-populations-transform-new-york/.
¹⁰⁵ Because of the relatively large size of the Hispanic NHPI population, the total population comparison is the better one
to make. However, given the current data limitations, we are not able to create this estimate for 2020. Once data are
available, we will be able to update this measurement.
¹⁰⁶ We can only see these charts for the 2010 Census because, as we noted, the data for 2020 are not yet available.
¹⁰⁷ It is important to note that if race was misreported in this way in the birth data, but not in the decennial census, we will
see a potential overcount in our analysis. This is one of the ways in which errors outside of the decennial census can cause
issues in interpretation of potential coverage errors using PA.
¹⁰⁸ This includes states such as Hawaii and California.
¹⁰⁹ Our choices were also informed by our knowledge of active state/local advocacy groups who can learn from findings
focused on their community.
¹¹⁰ The reason self-response can’t officially serve as a measure of decennial census accuracy is because the Census Bureau
uses techniques such as Nonresponse Followup and imputation to improve the count, especially in areas with low selfresponse rates.
¹¹¹ See, for instance:
https://static1.squarespace.com/static/5ef63030d9999309b65e3c91/t/6115277a7e4218049cc9d8b6/1628776334313/NHPI+De
mogrpahic+Profile+%28National%29.pdf, p. 21.
¹¹² Providing avenues for language support up front will help optimize self-response and remove potential cases from theNonresponse Followup workload. For more information, see letter from Asian American, Native Hawaiian and PacificIslander community-based organizations raising language support concerns: https://www.advancingjusticeaajc.org/sites/default/files/201810/AANHPI%20Census%20Language%20Support%20Program%20Letter%20October%202018%20Final.pdf.
See also senate letter raising concerns about language support program:
https://twitter.com/hansilowang/status/1075886626864357376. For example, unlike for the 2010 Census, when the Census
Bureau provided language assistance guides in several NHPI languages, the Bureau provided no language support in any
NHPI language for the 2020 Census.
¹¹³ For instance, being able to examine how self-response correlates with net coverage error at the county or city level cantell us a lot more than the state or national levels.
¹¹⁴ For more on Hawaii, see https://www.census.gov/library/stories/state-by-state/hawaii-population-change-betweencensus-decade.html.
¹¹⁵ https://www.census.gov/content/dam/Census/topics/research/erdman_bates_2017.pdf

85

Analysis: The Quality of Census Counts for Asian
American and NHPI Communities (continued)

NOTES¹¹⁶ For example: https://www.census.gov/content/dam/Census/library/working-papers/2019/demo/Hard-to-CountPopulations-Brief.pdf.
¹¹⁷ See, for instance: https://onlyinark.com/culture/marshallese-in-arkansas-from-the-islands-to-the-ozarks/ and the
Marshallesse Oral History Project (https://www.mei.ngo/mohp).
¹¹⁸ Renters are known to have lower response patterns, see for example:
https://www.census.gov/content/dam/Census/topics/research/erdman_bates_2017.pdf.
¹¹⁹ It will be interesting to see how these trends changed in 2020 once data are available from the 2020 Census because
this group grew significantly from a very small base in 2010.
¹²⁰ Note that there are relatively few census tracts that meet these conditions to draw strong conclusions about the selfresponses of NHPIs in Arkansas.
¹²¹ This means they can live and work in the U.S. legally. They are labeled as “nonimmigrants” but are not consideredcitizens or nationals, and they are not eligible for most federal benefits. See “A Community of Contrasts,”p. 21.
¹²² According to the 2020 Census.
¹²³ The number in 2020 appears to decline, but the data are not available for us to recode "Some Other Race" currently for
the Hispanic and NHPI community. This number would be much larger if we could report these data. A simple calculation,
without recoding those who report only "Some Other Race" and "Hispanic," shows at least 55,000 NHPI Alone or in
Combination in Los Angeles County in the 2020 Census. For more on NHPIs in the state of California, see:https://archive.advancingjustice-la.org/sites/default/files/A_Community_Of_Contrasts_NHPI_CA_2014.pdf.
¹²⁴ Utah is one of the three states where more than 1% of the population identifies as NHPI Alone. See:
https://www.census.gov/library/stories/2022/05/aanhpi-population-diverse-geographicallydispersed.html#:~:text=Hawaii%2C%20Nevada%2C%20and%20Alaska%20are,in%20combination%20with%20another%20r
ace.
¹²⁵ It will be important to see if this undercount persists in 2020.
¹²⁶ Interstate 15 runs through downtown Salt Lake City. It is the main interstate running through the city and begins in
San Diego, CA and ends in Northern Montana at the Canadian border.

86

Recommendations
We must look beyond traditional measures of decennial census quality toascertain the accuracy of Asian American and NHPI counts in the decennial
census.¹²⁷ We offer specific recommendations for how the Census Bureau andadvocacy groups can improve coverage and better prepare these populations forthe 2030 Census.
Most importantly, the census must take a “people-first” approach. This means the
Census Bureau must foreground our communities when making policy andoperational decisions, focusing specifically on those that have been missed byprevious censuses. Other factors, such as cost savings and “expediency,” cannot
take priority over people. One way to implement a people-first approach is toconsistently engage with interested stakeholders such as Community-Based
Organizations (CBOs) working with communities traditionally missed by thecensus. We must also invest in research that benefits smaller populations, such as
NHPIs. Finally, self-response is the preferred method of answering the census
because it allows respondents to self-identify. The implementation of each
recommendation that follows must be seen through this lens.
We organize our recommendations chronologically for changes that can bemade before, during, and after the taking of a census.

1. Researching and Planning for Assessing the Undercount:
Understanding and Improving Undercounts and Overcounts in both
Traditional and Non-Traditional Measures of Census Quality
The Census Bureau, academics, and CBOs must commit to improving how theymeasure and assess the quality of decennial census data. This must happen
relatively early in the decade, well before the fielding of the 2030 Census,
including for Asian American and NHPI communities.
Research how Asian American and NHPI communities can be incorporated
into Demographic Analysis
The Census Bureau, advocacy groups, and academics should research how Asian
American and NHPI communities can be better integrated into DA. The Census
Bureau can make use of existing historical data, such as births and deaths, that
can help improve the count for Asian American and NHPI communities (even if

87

Recommendations

some data for these groups must be combined to do so). Specifically, the Census
Bureau could examine if the data in pre-1997 OMB race categories for births anddeaths as well as those in the post-1997 categories could be used to create either
combined or separate estimates for the net undercount or overcount of the
AANHPI community. These estimates would likely only be available for a subset ofthe population (i.e., the younger age groups where data would be available onbirth records with post-1997 changes), much like the Census Bureau does for the
Hispanic and non-Hispanic DA estimates. These estimates would be immensely
useful for better understanding the quality of the decennial census for Asian
American and NHPI communities, as well as for planning for future censuses.
Finally, research should assess whether other records, such as Medicare, can beused for these populations as well.
Develop a more robust plan for the Post-Enumeration Survey in 2030
The Bureau must explore how it can provide more granular data through its PostEnumeration Survey in 2030. Most critically, the Bureau must give undercountrates by racial and ethnic group for each state. There is limited utility to only
providing racial and ethnic undercount rates at the national level. Racial and ethnic
undercount rates of larger sub-state jurisdictions are another important metric.
When coupled with the state-by-state racial and ethnic undercount data, this will
provide communities with a more fulsome understanding of how accurately theircommunities were counted.
This recommendation would require a substantial increase in the sample size for
the PES. However, this increase would serve all geographic areas and race groups,
leading to more accurate data after the decennial census is completed. It would
offer improvements on future decennial operations, targeting the groups with thegreatest needs and de-emphasizing groups with high response rates.
Focus on a larger set of potential census quality measures, and maintaintransparency in how the Census Bureau measures quality
Improving DA and the PES will significantly improve our understanding of thequality of the census for all communities, including Asian Americans and NHPIs.
However, they will never perfectly measure census quality. Instead, the Census
Bureau and other interested parties should continue to innovate and enhance ourunderstanding of the quality of a particular decennial census.
Postcensal population estimates can help measure census quality. After the 2020
Census, when the Census Bureau released state-level estimates compared toapportionment data, the Bureau suggested that population estimates could be
used in this way.¹²⁸ Therefore, the Census Bureau should not only use studies of88

Recommendations

the error of closure to improve estimates. They should also be used to strengthen
the decennial census. Operational metrics (including self-response rates) canfurther enhance census quality. Advocates and stakeholders would benefit if the
Census Bureau could tie race, even if only of the householder, to operational
metrics. The Census Bureau should maintain and increase their usage of all data
sources to better understand the quality of the census. Finally, they should discuss
the results of their studies with the public in a timely fashion.

2. Implementing the Census: Applying Research Findings to the 2030
CensusPromote Get Out The Count (GOTC) strategies for different subgroups
Overcounts and undercounts are not spread evenly either geographically or acrossracial and ethnic subgroups. This underlines the importance of local communities’and subgroups' efforts to GOTC. This includes: language support andcommunications, culturally and linguistically appropriate materials and outreach,and hiring community members to ensure trusted partners within the census
taker and partnership specialist ranks (positions within the Census Bureau).¹²⁹
Because no group is a monolith, a one-size-fits-all approach to census outreach will
result in further inaccuracies and wasted resources. Care must be taken
throughout the decennial census process–from planning to research to
implementation–to properly develop nuanced and targeted plans for reachingcommunities in culturally appropriate and effective ways.
For example, when developing research plans, specific attention should be given
to assessing the impact on smaller population groups (such as NHPIs). Anotheroption is to increase investment in attitude and messaging research that looks atboth racial and ethnic groups as well as different subgroups within each
community.¹³⁰ Finally, more attention is needed on the relationship between
funding spent on GOTC research and census response rates, outlining the efficacyof these efforts at different levels of geography and for different communities. The
Census Bureau should conduct this research early in the planning process toensure ample time to implement findings.
Enhance operations and the targeted use of administrative data
While administrative data should be used in responsible and cost-effective ways, it
must be done with a “people first” approach. That is, data should be used to lower
the cost of the census for those that are easily found and counted in administrative
records and (most likely) the census. Instead of using funding to count those easily

89

Recommendations

enumerated, funding should be allocated to count people who are not likely toappear in administrative data (and who are also not likely to respond to thedecennial census). These groups would include, but are not limited to, non-citizens,
renters, and those without broadband access.¹³¹ For those groups, responses
should be secured directly from the household, especially during Nonresponse
Followup, with administrative records generally used as a last resort.

3. After Fielding the Census: Executing Best Practices in Post-Fielding
Activities to Improve the Mechanics of the Count
Develop best practices for processing and resolving omissions andduplications
Discussions of decennial census quality often focus on undercounts. There is lessfocus on overcounts from duplications or other erroneous enumerations.¹³² Much
like undercounts, these can hamper the quality of the decennial census andimpact decennial census outcomes––including funding and politicalrepresentation. We cannot have a fair and equitable decennial census process
without focusing on both omissions and duplications. Therefore, the Census
Bureau should research how to address, and correct for, duplications or othererroneous enumerations that lead to overcounts along with its ongoing focus onaddressing undercounts.
Correcting for differential undercounts by race
Identifying and gaining insight into potential undercounts and overcounts is a firststep in resolving the problem. But there are also opportunities to fix differential
undercounts retroactively. For example, after the 2020 Census, the Census Bureau
applied a fix to postcensal population estimates, using DA to adjust the estimatesbase to account for the undercount of young children.¹³³ Similar changes could beapplied by race and ethnicity. This could improve the overall equity and fairness ofthe decennial census and downstream processes (such as population estimates
and the American Community Survey). Additionally, the Census Bureau should
research potential options to correct differential undercounts at lower levels ofgeography in the decennial census. Finally, the Bureau should engage in arigorous discussion with interested stakeholders to determine the best way toaddress these issues.
Stakeholder engagement and debriefing
The Census Bureau should formally debrief relevant stakeholder groups,
discussing plans and sharing suggestions for how to address each of the above
issues. These briefings should begin early in the planning process and continue
90

Recommendations

throughout the implementation phases. These groups should include, but should
not be limited to, the National Advisory Committee and the Census Scientific
Advisory Committee.
Enhancing partnerships for research and development
The Census Bureau should increase investment in partnering with researchers todetermine the causes of overcounts and undercounts, particularly as they impact
different racial and ethnic groups. More research is needed on the specific barriers
to participation for differential racial and ethnic groups, as well as subgroups.
Achieving this understanding is the first step towards making meaningful changes
to eradicate differential undercounts.

NOTES¹²⁷ As we have shown throughout this paper, traditional measures of census quality do not provide enough detail for
either of these groups. See also: https://link.springer.com/book/10.1007/978-3-030-10973-8 for a discussion of what is and is
not covered in traditional measures of census quality for Asian Americans and NHPIs.
¹²⁸ Specifically, looking at larger differences between the estimates and the census, while not proof of census miscounts,
provides us with evidence of the need for futher examination. According to A Preliminary Analysis of U.S. and State-Level
Results from the 2020 Census, “Instead, comparisons between the two are generally undertaken to evaluate the quality ofthe estimates and ascertain the effectiveness of method changes applied over the course of the previous decade. The
difference between the census and the estimates is called the ‘error of closure,’ and it is used to inform future
methodological improvements and research for the estimates."
https://www.census.gov/content/dam/Census/library/working-papers/2021/demo/pop-twps0104.pdf, p. 5.
¹²⁹ See, for example, https://www.countusin2020.org/; https://funderscommittee.org/learning-evaluations/;
https://www.advancingjustice-aajc.org/sites/default/files/202103/JCY%20House%20Oversight%202020%20Census%20Jan%209%202020%20Hearing%20Final_0.pdf;
https://www.advancingjustice-aajc.org/sites/default/files/201805/Advancing%20Justice%20AAJC%20testimony%20May%208%202018%20OGR%20Hearing%20Final.pdf; andhttps://www.advancingjustice-aajc.org/sites/default/files/201808/Advancing%20Justice%20Affiliation%20FRN%20Comments%20re%202020%20Census%208.7.2018.pdf.
¹³⁰ The Census Barriers, Attitudes, and Motivators Study is a good example of this type of work, but greater emphasis
could be placed on truly testing the efficacy of messaging. https://www.census.gov/programs-surveys/decennialcensus/decade/2020/planning-management/plan/communicationsresearch/2020_cbams.html#:~:text=The%202020%20Census%20Barriers%2C%20Attitudes%2C%20and%20Motivators%20S
tudy,the%202020%20Census%20Integrated%20Partnership%20and%20Communications%20Program¹³¹ See, for example: https://mccourt.georgetown.edu/news/who-is-missing-from-administrative-data/;
https://www.census.gov/content/dam/Census/library/working-papers/2014/adrm/carra-wp-2014-08.pdf;
https://www.urban.org/research/publication/internet-first-or-internet-choice-identifying-factors-predict-low-selfresponse-rates-2020-census.
¹³² While relatively less time and energy is spent on overcount research and development, this research does still exist in
every decennial census. See, for example: https://nces.ed.gov/FCSM/pdf/2003FCSM_Fay.pdf andhttps://www.urban.org/sites/default/files/publication/100324/assessing_miscounts_in_the_2020_census.pdf.
¹³³ https://www2.census.gov/about/partners/cac/nac/meetings/2022-05/presentation-blended-base-for-populationestimates.pdf

91Conclusions
The challenges of understanding the quality of decennial census data for Asian
American and NHPI communities is evidenced by the fact that two traditional
measures, DA and the PES, do not provide any information on Asian Americans
and NHPIs (DA) or only give us national-level information on these communities(the PES). Given the paucity of data, we developed a new methodology, PA, tobetter understand the quality of the decennial census for Asian Americans and
NHPIs. This measure allows us to understand the quality of the census for thesecommunities at lower levels of geography. To achieve this, we compared the
Census Bureau’s postcensal population estimates to the decennial census. Thisnew measure provides insight into the distribution of census quality for Asian
American and NHPI communities. Most importantly, it offers an example of how
to improve on the overall assessment of census quality by adding another usefulmeasure.
While it is possible to determine overcounts and undercounts from traditional
measures of census quality at the national level, significant geographic anddemographic variability is hidden by these top-level numbers. For instance, inboth 2010 and 2020 our methodology suggested Asian Americans and NHPIs
both had estimated overcounts at the national level. But there were states that
did not follow the pattern. Specifically for the Asian American community, somestates in the Upper Midwest, the Northeast, and the South showed undercounts.
The Census Bureau should focus special attention on these areas in the 2030
Census to correct for previous overcounts and undercounts.
We found similar patterns for the NHPI community. Many states with highpopulations of NHPIs aligned with the national average by showing estimatedovercounts in both 2010 and 2020. However, some states in the Upper Midwest,
the Northeast, and the South showed undercounts. Some very large counties for
the NHPI community, such as Los Angeles and Hawaii counties, showedestimated undercounts in 2020. The Census Bureau should focus special
attention on these areas in the 2030 Census to correct for previous overcounts
and undercounts.

92

Conclusions

Within the Asian American community, young children are undercounted––whichaligns with data on other racial groups. This undercount of young children is notshown in the data for NHPIs––a topic worthy of further research.
Our findings suggest that the Census Bureau needs to make significant
investments in several areas. First, the Census Bureau should improve how
undercounts and overcounts are assessed in both traditional and non-traditional
measures of census quality through research and planning the next round ofassessments. Second, the Bureau should implement the census and apply
research findings to the 2030 Census. Finally, the Bureau should review the census
by executing best practices in post-fielding activities to improve the mechanics ofthe count.
This report raises new questions and opens further avenues for research. Additional
information is needed on undercounts and overcounts at lower levels ofgeography for other racial groups; knowing where errors in coverage are most
likely to occur is an essential first step in addressing undercounts and overcounts
for other historically marginalized groups. We also need to know more about how
subgroups within the Asian American and NHPI community respond to thecensus. We urge researchers and the Census Bureau to develop new
methodologies that can examine how racial subgroups' responses impact
overcounts and undercounts. Finally, the relationship between decennial census
coverage and self-response is worthy of further study. Researching and solving
these questions will only improve outcomes for the Asian American and NHPI
communities, which in turn will improve accuracy of the census and likewise
strengthen our democracy.

93

Appendix 1. The Development of Modified
Race for 2020 Census Data for this Report
In order to make comparisons between decennial census data and population
estimates, it is necessary to reconcile the race categories available in the census
data with race categories that appear in the datasets used to produce population
estimates.
Since the 2000 Census, respondents have been able to select one or more of thefive current Office of Management and Budget (OMB) race groups (White, Black,
American Indian or Alaska Native, Asian, or Native Hawaiian or Pacific Islander).
OMB gave special dispensation to the decennial census and ACS only to utilize a"Some Other Race" category due to statutory requirements that this category be
included. "Some Other Race" may be selected on its own, or in tandem with theother five race categories. Because the administrative data sources that the
population estimates use do not include this race category, it is necessary to recode“Some Other Race” respondents from the decennial census data into the other five
OMB categories (or one of the multiple race categories made up from those). This
file is called the Modified Race (MR) file.
For the 2000 and 2010 Censuses, we are able to obtain the crosswalk between raceon the census and modified race from the MR files released by the Census
Bureau.¹³⁴ The Census Bureau uses a set of rules to recode the "Some Other Race"data in this MR, specifically:¹³⁵
1. For all people who did not respond with “Some Other Race,” no changes weremade.
2. For people who responded “Some Other Race” in combination with one ormore of the other five OMB race categories, “Some Other Race” was simply
removed, leaving only the OMB race category responses.
3. For people who only reported “Some Other Race,” the Census Bureau allocatedrace back to someone from the same household. First, they ensured that theperson matched on Hispanic origin. Then, race was allocated back to one of thefive OMB race categories (or a combination thereof). Or, if no such person
existed within the household, “Some Other Race” respondents were allocatedfrom hot deck imputation (a process that finds similar people in nearby areas touse as “donors” for race characteristics).

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Appendix 1. The Development of Modified Racefor 2020 Census Data for this Report (continued)

Currently, the U.S. Census Bureau does not have the data to create a MR file for the2020 Census. According to the Bureau, they currently plan to release a MR file once
the Demographic and Housing Characteristics file is released, which is slated for
May of 2023.¹³⁶ Because of this gap in data availability, and the need to compare
race groups in the interim for PA in this report, we created our own MR file. Unlike
the demographers at the U.S. Census Bureau, we do not (and for confidentiality
reasons, should not) have access to individual-level data, so we use county-level
aggregates with the following rules to create this file:
1. For all people who did not respond with “Some Other Race,” no changes weremade.
2. For people who responded “Some Other Race” in combination with one or moreof the other five OMB race categories, “Some Other Race” was simply removed,
leaving only the OMB race category responses (or the combination thereof).
3. For those who only reported “Some Other Race,” we allocated these back to oneof the five OMB race groups (or a combination thereof). We used the countylevel proportion from the 2010 MR file of people that were coded from “SomeOther Race” to each of the 31 race groups¹³⁷ that are used in the population
estimates: White, Black, American Indian or Alaska Native, Asian, Native
Hawaiian or Pacific Islander, and Multiple Races. We then use controlled
rounding, a raking procedure, to ensure our county total populations do notchange when we undertake this recoding.
Once we have a final file with “Some Other Race” recoded to the 31 possible racegroups at the county level, we sum these data into both race “Alone” and race
“Alone or in Combination” groupings. These data are then compared to the
population estimates from the corresponding vintage year in order to determine
differences between the decennial census and the population estimates.

NOTES¹³⁴ For 2000 data see: https://www.census.gov/data/datasets/2000/demo/popest/modified-race-data-2000.html.
For 2010 data see: https://www.census.gov/data/datasets/2010/demo/popest/modified-race-data-2010.html.
¹³⁵ For further information on the methodology for developing the 2010 MR file: https://www2.census.gov/programssurveys/popest/technical-documentation/methodology/modified-race-summary-file-method/mrsf2010.pdf.
¹³⁶ https://www2.census.gov/programs-surveys/decennial/2020/program-management/DHC%20Timeline¹³⁷ https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2000-2010/mr2010.pdf

95